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Case Study of Crisis and an Affirmation of Character

The History of Starbucks Coffee Company's Anti-Bias Efforts

This is a story of a business that has always tried to be a different kind of company. It’s a story of an incident of racial bias in one of the brand’s stores—and how that moment galvanized a national effort to confront racial bias, and reaffirm an intention to ensure all Starbucks stores are places that are welcoming of all. It’s told through a diverse set of voices: the Starbucks leaders and team members, critical advisors, and design partners who led the efforts over an 18-month period.

Where Does This Story Start?

A Bold Move Fueled by Critics, Believers and Partners

A massive design effort, color brave, not color blind, shared experiences are the motivation and common language, whole-human design: the only hope, realization #1: the role of starbucks as a welcoming ‘third place’ needed a serious examination. , realization #2: leaders are learners, too., realization #3: building a sense of goodwill would be vital—we had to create a safe space., realization #4: representation has to become our modus operandi., realization #5: when given the chance, humans rise to their best selves., may 29: a day of conversation, personal reflection and peace.

WE’LL SEE YOU TOMORROW At Starbucks we are proud to be a third place—a place between home and work where everyone is welcome. A place where everyone feels they belong. Today, our store team is reconnecting with our mission and with each other. We are sharing our ideas about how to make Starbucks even more welcoming.  We look forward to seeing you when we reopen at ___________.

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The Starbucks Incident: a crisis management case study

Erik Bernstein May 18, 2018 crisis management 2 Comments

The Starbucks incident: a crisis management case study

By Rick Kelly

Shortly after opening its first store in 1971, Starbucks began to distinguish itself as a do-the-right-thing kind of retailer. It offered full health care and stock options to employees, embraced diversity and inclusion, created a foundation to support its communities, located stores in underserved areas, promoted certified Fairtrade products, established ethical coffee-sourcing standards and built farmer support centers in coffee-growing regions. Along the way, it also rewarded its investors. Following its initial public offering in 1992, Starbucks has had multiple two-for-one stock splits.

By nearly any measure, Starbucks has been ultra-successful, with now about 28,000 stores worldwide and unmatched influence in the supplier markets. Up until the Philadelphia incident, it’s hard to imagine anyone being mad at Starbucks. Clearly it has walked the social responsibility talk. But when a request to use a restroom in the Philadelphia store escalated into the arrests of the two men who had come there to meet a friend, the public reaction was loud and furious.

A cell phone video of the arrests went viral, and it instantly attracted worldwide attention and accusations of racism. The video showed that the arrestees had done nothing to merit such a fate. What’s a crisis manager to do?

The first consideration in managing any crisis is to avoid making the situation worse. Starbucks accomplished that by immediately recognizing the threat to its reputation , responding quickly, issuing an unequivocal apology (as opposed to “We’re sorry  if  we offended anyone”) and by flying across the country to deliver apologies in person to the men who were arrested.

The company also succeeded in positioning the incident as a “teachable moment.” Starbucks CEO Kevin Johnson, in an ABC interview on the Monday following the incident, repeated the apology, took responsibility and said he wanted to apologize in person to the men and “invite them to join me in finding a way to solve this issue.” The men who had been arrested agreed they “want to make sure this situation doesn’t happen again.” Their settlement this week with the City of Philadelphia is a testament to their constructive intentions.

In the face of an organized protest at the site of the arrests and calls for a national boycott of the company, Johnson announced plans to close 8,000 U.S. stores on May 29 to provide employees with bias sensitivity training. In multiple interviews, he continued to apologize and pledged to identify and address the factors that led to such a dire result. He also insisted that a half-day of sensitivity training was only a first step.

Johnson was right to accept responsibility for the incident. The company’s policies, procedures, training and culture fell short of preventing an outcome that should not have occurred and ran counter to one of the organization’s stated values of “creating a culture of warmth and belonging, where everyone is welcome.”

Whether a place where everyone is welcome should include restroom access for non-customers may be debatable, but insisting that visitors buy something upon arrival or face forcible removal is not a welcoming gesture.  We’re guessing that the company will spend some time thinking about that.

In any event, we offer the following recommendations to those who imagine having to handle a crisis of their own:

• Pay attention to issues that become the subject of heightened public sensitivity. Racial bias and sexual misconduct are leading the pack these days.
• Assess (or hire someone to assess) the vulnerabilities of your organization, and devise a plan for handling the most likely worst-case crisis scenarios.
• Build reputational equity by being responsible as an organization to customers, employees, other stakeholders and the public at large. Starbucks’ record for walking the talk should earn it another chance with most people.
• Assume that it’s not a question of  if  a crisis will occur, but when. And when it does, act quickly and don’t equivocate or make excuses.

Depending on the nature of the crisis, some stakeholders may turn away for good. At the same time, handling a crisis well can strengthen an organization’s relationships and strength.

How will this episode turn out? There’s no doubt that Starbucks was in the wrong, but it acknowledged its failures quickly, accepted responsibility and devised a remedy (or at least a first step). We’re betting that Starbucks will be selling lattes for a long time, and that other organizations will use this episode as a template the next time they find themselves in times of trouble.

Rick Kelly is Triad’s VP of Strategic Communication and directs the firm’s crisis management practice. You can reach him at  [email protected] .

Interested on talking to a crisis management expert now? Click this link for ways to get in touch.

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Navigating the Storm: Starbucks Crisis Management Case Study

Is crisis management a make-or-break factor for businesses? 

In this blog post, we delve into a captivating case study: Starbucks crisis management. 

With a reputation for providing quality coffee and fostering a welcoming environment, Starbucks faced a significant crisis that put its brand image at stake. 

The way a company responds to a crisis can have far-reaching consequences, impacting its customer loyalty, shareholder confidence, and overall success. 

Join us as we examine the strategies employed by Starbucks to navigate this crisis, evaluate their effectiveness, and uncover valuable lessons for businesses facing similar challenges.

Let’s further unpack the topic through Starbucks crisis management case study

Background of Starbucks

Aglobally recognized coffeehouse chain, was founded in 1971 in Seattle, Washington. The company initially started as a single store specializing in high-quality coffee beans and equipment. 

However, it was in the 1980s when Howard Schultz, the current chairman emeritus, joined Starbucks and revolutionized its business model. Schultz envisioned Starbucks as a “third place” between home and work, a community gathering spot where people could enjoy premium coffee. 

Under his leadership, Starbucks expanded rapidly, opening stores not only across the United States but also worldwide. 

The company’s growth was fueled by a strong commitment to sourcing and roasting the finest Arabica coffee, ensuring consistency in the customer experience, and embracing innovation in its products and store designs and implementing change initiatives as a part of their continuous improvement approach.

Starbucks’ Reputation and Brand Image

Starbucks has meticulously cultivated a reputation as a purveyor of quality coffee and an immersive, inviting atmosphere. The company’s brand image is associated with expertise in coffee, ethically sourced beans, and a commitment to social and environmental responsibility. 

Starbucks has positioned itself as a “third place” where customers can relax, work, or socialize while enjoying their favorite beverage. The brand’s iconic logo, the green Siren, has become synonymous with Starbucks and is instantly recognizable worldwide. 

The company’s dedication to providing exceptional customer service and its inclusive culture has also contributed to its positive reputation. 

Over the years, Starbucks has become a symbol of premium coffee experiences and a beloved brand embraced by millions of people around the globe.

The crisis that unfolded at Starbucks was centered around an incident in one of its stores that sparked widespread controversy and public outrage. 

In April 2018, a video surfaced on social media showing two black men being arrested at a Starbucks store in Philadelphia. The incident occurred when the men, who were waiting for a friend, were denied access to the store’s restroom. The store manager called the police, alleging that the men were trespassing. 

The video quickly went viral, drawing attention to issues of racial profiling and discrimination.

The events leading up to the crisis can be traced back to a combination of store policies and employee training practices. Starbucks had a policy in place that required non-paying customers to make a purchase before using the store facilities. 

This policy, coupled with the discretion given to store managers, created a situation where individual judgment played a role in determining who was allowed to use the facilities. In this case, the store manager’s decision to call the police escalated the situation, drawing significant public scrutiny.

Impact of the Crisis on Starbucks

The incident had a profound impact on Starbucks as a brand and a company. Immediately following the video’s circulation, there was a widespread public backlash, with accusations of racial bias and calls for a boycott of Starbucks. 

The incident sparked protests, garnered extensive media coverage, and led to a significant reputational hit for the company. Starbucks’ longstanding reputation as an inclusive and welcoming space was severely tarnished. 

Moreover, the incident shed light on broader issues of racial inequality and discrimination within the retail industry, forcing Starbucks to confront these systemic challenges head-on. 

The crisis not only presented an immediate challenge for Starbucks’ public image but also posed a critical test for the company’s crisis management capabilities.

Crisis Management Strategies Used by Starbucks  

Following are the key aspects of crisis management strategies used by Starbucks:

Immediate Actions

Immediate actions Taken by Starbucks Recognizing the urgency of the situation, Starbucks swiftly responded to the crisis. Within 24 hours of the incident, the company’s CEO, Kevin Johnson, publicly apologized to the individuals involved and expressed deep regret for what had transpired.

Starbucks took immediate action by announcing the closure of more than 8,000 company-owned stores across the United States for a half-day of racial bias training. This decision demonstrated a commitment to addressing the underlying issues and implementing tangible measures to prevent similar incidents in the future.

Communication Channels Used 

Starbucks utilized multiple communication channels to address the crisis effectively. The company made extensive use of social media platforms, such as Twitter and Facebook, to disseminate its messages. Starbucks posted public apologies and updates on its official social media accounts, engaging directly with customers and the general public.

Additionally, the CEO and Chairman conducted several media interviews to convey the company’s stance and commitment to resolving the crisis. Starbucks also employed traditional media outlets, press releases, and official statements to ensure a wide reach and consistent messaging across various communication channels. This comprehensive approach aimed to provide timely and transparent information while actively engaging with stakeholders during the crisis.

Employees Training and Policy Changes  

Starbucks recognized the need to address implicit bias and promote inclusivity among its employees. The company implemented a comprehensive training program focused on racial bias awareness and prevention.

This initiative, known as “Starbucks Bias Training,” involved closing over 8,000 company-owned stores across the United States for a half-day to provide racial bias education to 175,000 employees. The training sessions were designed to create awareness of unconscious biases, foster empathy, and equip employees with strategies to ensure an inclusive and welcoming environment for all customers.

Revising Company Policies and Guidelines 

Alongside employee training, Starbucks undertook a thorough review of its policies and guidelines to ensure they aligned with the company’s commitment to diversity and inclusion.

One significant policy change was the revision of the “Third Place Policy,” which governs customer access to Starbucks facilities. The updated policy clarified that customers are welcome to use Starbucks spaces, including restrooms, regardless of whether they make a purchase.

By eliminating potential ambiguity, Starbucks aimed to eliminate situations where individual discretion could lead to discriminatory practices. This policy change aimed to create a more inclusive environment and prevent similar incidents in the future.

Through these employee training initiatives and policy changes, Starbucks sought to address the root causes of the crisis and build a more inclusive and welcoming culture within its stores. By prioritizing education and revising policies, Starbucks aimed to prevent bias and discrimination, demonstrating its commitment to creating a safe and inclusive environment for all customers.

Rebuilding Trust and Reputation 

Rebuilding trust and reputation is a critical process for organizations that have experienced a crisis or faced significant challenges. It involves implementing long-term strategies to regain the trust of stakeholders, rebuild a positive brand image, and restore confidence in the organization’s values and actions.

Starbucks engaged with local communities and various social initiatives to rebuild its brand image. The company actively participated in community events, supported local organizations, and initiated social impact programs. These efforts showcased Starbucks’ commitment to social responsibility and its desire to positively contribute to the communities it serves.

Starbucks maintained consistent communication and brand messaging throughout the crisis and beyond. The company continued to communicate its commitment to diversity, inclusivity, and social responsibility. By consistently reinforcing these values in its messaging and actions, Starbucks aimed to rebuild trust and ensure that its brand image aligned with its core values.

Role of Howard Schultz, Chairman of Starbucks in crisis management 

The role of Howard Schultz, the Chairman of Starbucks was instrumental in navigating the company through the challenging situation. 

Here are key aspects of his involvement:

• Setting the Tone: As a respected and influential figure within the company, Howard Schultz set the tone for Starbucks’ response to the crisis. His leadership and guidance provided a framework for the company’s actions and messaging. Schultz’s reputation as a visionary leader and his deep understanding of Starbucks’ core values and culture helped shape the crisis management strategy.
• Public Apology and Personal Accountability: Howard Schultz took personal accountability for the crisis, publicly apologizing on behalf of Starbucks. His willingness to accept responsibility demonstrated a sense of ownership and commitment to rectifying the situation. By publicly acknowledging the incident and expressing genuine regret, Schultz exemplified the values of transparency and humility. In response to this crisis Howard Schultz said “I’m embarrassed, ashamed. I think what occurred was reprehensible at every single level. I take it very personally, as everyone in our company does, and we’re committed to making it right.”
• Engaging with Stakeholders : Schultz actively engaged with various stakeholders, including customers, employees, and the media, to address concerns and provide reassurance. He participated in media interviews, where he openly discussed the incident, Starbucks’ commitment to diversity and inclusion, and the steps the company was taking to prevent similar incidents. This direct engagement helped rebuild trust and demonstrated Starbucks’ dedication to resolving the crisis.
• Initiating Comprehensive Changes: Schultz played a key role in initiating comprehensive changes within Starbucks to prevent future incidents and promote a more inclusive environment. He championed the decision to close stores for racial bias training, emphasizing the importance of education and awareness. Schultz also supported the revision of company policies and guidelines to eliminate potential biases and ensure equal treatment for all customers.
• Long-Term Vision and Brand Preservation: As the Chairman, Schultz had a deep understanding of the Starbucks brand and its long-term vision. During the crisis, he ensured that the company’s actions aligned with its core values and overarching goals. By prioritizing the preservation of Starbucks’ brand reputation and maintaining its commitment to social responsibility, Schultz played a crucial role in guiding the crisis management strategy.

05 Lessons Learned form Starbuck’s crisis management 

Here are five key lessons learned from Starbucks’ crisis management:

Proactive Crisis Preparedness

Starbucks’ crisis management highlighted the importance of proactive preparedness. Being ready to respond swiftly and effectively to crises requires ongoing risk assessment, scenario planning, and robust crisis management protocols. Businesses should invest in training, communication plans, and policy reviews to anticipate and mitigate potential crises before they occur.

Timely and Transparent Communication

Starbucks’ prompt and transparent communication during the crisis played a vital role in mitigating reputational damage. Openly acknowledging the issue, providing regular updates, and engaging with stakeholders helped maintain trust and demonstrate a commitment to addressing the problem. Clear, consistent, and timely communication is essential in crisis situations to avoid misinformation and public backlash.

Employee Training and Empowerment

Starbucks’ crisis underscored the significance of comprehensive employee training programs. By educating employees about topics like implicit bias and fostering inclusivity, companies can empower their workforce to handle diverse situations and prevent discriminatory incidents. Regular training sessions can equip employees with the skills and knowledge necessary to uphold the company’s values, contributing to a more inclusive and respectful environment.

Policy Review and Adaptation

Starbucks’ crisis led to a review and revision of company policies and guidelines. It highlighted the need for businesses to periodically evaluate their policies to eliminate potential biases and ensure equal treatment for all customers. Regular policy reviews and adaptations based on societal changes and customer expectations can help organizations stay aligned with evolving standards and maintain a positive reputation.

Leadership Accountability and Responsibility

Starbucks’ crisis management emphasized the importance of leadership accountability and responsibility. When leaders take personal accountability, apologize sincerely, and actively participate in resolving the crisis, it demonstrates a commitment to rectifying the situation. Leadership involvement fosters trust, reassures stakeholders, and reinforces the organization’s commitment to its values.

By learning from Starbucks’ crisis management, businesses can better prepare for potential crises, develop effective communication strategies, prioritize employee training, review and adapt policies, and demonstrate leadership accountability. These lessons serve as a valuable guide for businesses aiming to navigate crises successfully and protect their reputation and stakeholders’ trust.

Final Words 

Starbucks crisis management case study provides valuable insights into how a company can effectively handle a challenging situation that threatens its reputation and brand image. The case highlighted the importance of timely response, transparent communication, and proactive measures to address the underlying issues. Starbucks demonstrated leadership accountability, employee training, and policy changes to foster inclusivity and prevent future incidents.

By learning from Starbucks’ crisis management case study, businesses can better prepare themselves to handle crises effectively, protect their reputation, and maintain the trust of their stakeholders. The lessons learned serve as a valuable guide for businesses of all sizes and industries, emphasizing the importance of crisis preparedness, communication, employee training, policy review, and leadership accountability

About The Author

Tahir Abbas

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Ideas Made to Matter

What Starbucks got wrong — and right — after Philadelphia arrests

Tom Relihan

May 18, 2018

On May 29, Starbucks will close more than 8,000 U.S. stores for an afternoon of racial bias training for 175,000 employees. The move, expected to cost $12 million, comes after an April 12 incident when two black men were arrested after asking to use a Philadelphia Starbucks restroom without making a purchase.

While it’s unlikely the company will be able to eradicate the effects of unconscious racial bias among its employees in a single afternoon, MIT Sloan senior lecturer Roberta Pittore said the decision to close the stores for training was the right call.  As recently as Friday afternoon, the company issued another apology after an employee at a California store wrote a racial slur on a customer's cup.

“Something is better than nothing, more is better than less, and sooner is better than later,” Pittore, who studies communication as it influences decision-making in organizations, said. “I think what it does achieve from Starbucks’ point of view is that it changes the discussion from ‘What did our employee do that was offensive,’ to ‘How can we learn and how can we change?’”

Swift, specific, and sincere

The rise of social media has given people the ability to spread news in milliseconds, and a corporate response needs to match that pace, Pittore said. But, any response also needs to be specific and sincere.

In its initial attempt to address the controversy that day, the company fell flat on at least two of those counts. In releasing a general statement acknowledging the situation and promising a policy review, but failing to mention concerns about racial bias, the company’s response was swift, but it wasn’t specific or sincere enough.

That first statement was followed later in the day by a longer one from CEO Kevin Johnson, who took a more targeted tone, describing the outcome as “reprehensible” and reaffirming the company’s opposition to discrimination and racial profiling. Johnson also released a video in which he took personal ownership for the incident.

Pittore said the revised response indicates Starbucks’ realization that the incident carried more gravity than the company’s leadership had initially thought.

“[A generalized statement] doesn’t seem as sincere — and it isn’t. It isn’t addressing the problem. It’s just saying, ‘We’re good guys … we have good intentions,’” Pittore said. “Well, everyone has good intentions, so what are your good intentions about this very real, very specific, very tangible incident?”

Soon after, Johnson traveled to Pennsylvania to meet with the two men and offer a face-to-face apology. Pittore said that move, too, was appropriate, given the climate surrounding the incident. In May, the company also reached a financial settlement with the pair for an undisclosed amount of money and a promise to help them complete their bachelor’s degrees through the company’s employee tuition assistance program.

“The company appropriately should find out from them, ‘How do we make this up to you?’” Pittore said.

When public access is part of your business model

At the core of Starbucks’ brand is the idea that its coffee shops can serve as a “third place” for meet-ups, studying, or work on the go. With the arrests, a single store manager’s decision saw that carefully-crafted persona of a socially progressive and inclusive community hangout begin to unravel.

“They’re not selling you a cup of coffee. They’re saying, ‘We’re the part of the community that you’re in when you’re not at the office and you’re not at home,’” Pittore said. “When you think about that, you have to think about the larger community and about ‘How do we, as a public, want that third place to be. Bottom line, it should feel safe. It should feel welcoming, and that’s really the crux of the problem.”

That problem isn’t unique to Starbucks, either, Pittore noted — it’s a societal problem that came to a head in a very visual way in Philadelphia.

“The specific actions of that one specific employee uncovered a larger issue of racial bias, frankly, in our culture. If you narrow it down, you have an incident that you can’t sweep under the rug,” she said. “The question for Starbucks is ‘What do we do? We didn’t create this problem, but we’re part of this society where this is happening, and if we’re going to be that third place, what is that third place going to look like?’”

Moving forward, Pittore said Starbucks should review its policies, the composition of its board and leadership, its gender, race, and age demographics among its employees, and its wage structures to determine how socially conscious the company really is and decide where, on that spectrum, it wants to be.

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Case Study Of Starbucks: How Starbucks Became The Coffee King?

Supti Nandi

Updated on: April 25, 2024

Starbucks, a brand that became synonymous with coffee has created a sensation in the world with its coffeehouse culture. Have you wondered how? Well, to answer this question we will delve into the case study of Starbucks.

Stay tuned!

(A) Starbucks: A Brief Overview

Let’s buckle up for a Starbucks journey! Founded back in 1971, this coffee giant now reigns supreme as the world’s largest coffeehouse chain, with its home base in the city of Seattle, Washington.

Before diving deeper into the Starbucks case study, let’s have a look at the company’s profile-

Fast forward to November 2022, and you’ve got Starbucks waving its coffee wand in a staggering 35,711 stores across 80 countries. And when you zoom in on the U.S., you’re looking at a whopping 15,873 Starbucks hotspots. 

Here’s the scoop – over 8,900 are Starbucks-run, and the rest are running under licensed partnerships.

Now, let’s talk coffee vibes. Starbucks is the unsung hero of the second wave of coffee culture, dishing out an array of coffee delights. Think hot espresso, chill Frappuccinos, and a lineup of pastries and snacks that’s strong enough to trigger your taste buds.

Oh, and did you know some Starbucks treats are exclusive to certain locations? How? You may wonder. Well, here’s a bonus – most Starbucks joints worldwide offer free Wi-Fi. Coffee and connectivity – a match made in heaven.

So there you have it – the Starbucks saga! 

(B) Business Overview of Starbucks Case Study

Understanding the business perspective is one of the essential parts of the Starbucks case study. Reason? You will get to know how Starbucks is performing in the market in terms of financials and business.

Go through the table given below-

In today’s date, the coffee giant is flexing a market capitalization of a whopping $105.82 billion – that’s some serious coffee beans.

Now, rewind to 2023, and Starbucks made it rain with a revenue of $35.976 billion. But what about the nitty-gritty? Operating income in 2022 hit $4.62 billion, while net income settled at $3.28 billion. These aren’t just numbers; they’re the financial pillars of Starbucks.

That’s not all!

Hold onto your coffee cups; we’re diving into assets and equity. Total assets in 2022 clocked in at $27.98 billion – that’s like a treasure chest of coffee goodness. But here’s a twist – total equity dipped to -$8.70 billion. It’s like a plot twist in a coffee-fueled drama.

Now, let’s talk about expenses and profits. In 2023, expenses tallied up to $30.584 billion, but here’s the kicker – profits soared to $25.108 billion. 

That’s like balancing a delicate espresso shot with a mountain of whipped cream.

In a nutshell, Starbucks isn’t just brewing coffee; it’s a financial powerhouse, stirring up a caffeinated storm in the business world.

(C) History of Starbucks: Timeline & Key Events

Coming to the third part of the Starbucks case study, let’s delve into the history of Starbucks-

Founded in 1971 by Jerry Baldwin, Zev Siegl, and Gordon Bowker at Seattle’s Pike Place Market, Starbucks underwent pivotal changes in ownership and leadership. In the early 1980s, Howard Schultz acquired the company and transformed it into a coffee shop, introducing espresso-based drinks after being inspired during a business trip to Milan, Italy.

Schultz served as CEO from 1986 to 2000, orchestrating an expansive franchise expansion across the West Coast.

Orin Smith succeeded Schultz, focusing on fair trade coffee and boosting sales to US$5 billion. Jim Donald took the helm from 2005 to 2008, overseeing substantial earnings expansion. Schultz returned during the 2007–08 financial crisis, steering the company towards growth, expanded offerings, and a commitment to corporate social responsibility. Kevin Johnson assumed the CEO role in 2017.

In March 2022, Starbucks announced Schultz’s return as interim CEO in April 2022, with Laxman Narasimhan appointed to succeed him in April 2023. Narasimhan assumed the position earlier, in March 2023.

Beyond beverages and food, Starbucks stores offer official merchandise and select locations to provide “Starbucks Evenings” with beer, wine, and appetizers. The company’s products, including coffee, ice cream, and bottled drinks, are available in grocery stores globally. The Starbucks Reserve program, initiated in 2010 for single-origin coffees and high-end shops, has evolved. Starbucks operates six roasteries with tasting rooms and 43 coffee bars. 

The company faced controversies but maintains substantial brand loyalty, market share, and value. As of 2022, Starbucks ranks 120th on the Fortune 500 and 303rd on the Forbes Global 2000.

(D) Significance of Logo in Starbucks Case Study

Let’s delve into the details of the Starbucks logo evolution. In its inception in 1971, the original Starbucks logo featured a complex design comprising a two-tailed mermaid or siren, encompassed by a wordmark. This design was a visual nod to the brand’s early identity and origins. The mermaid, with its twin tails, was a dual representation of the sea and Seattle, the birthplace of Starbucks.

As the brand progressed, the logo underwent a significant transformation. The evolution saw a shift towards simplicity, as the wordmark surrounding the mermaid was phased out. This marked the beginning of the modern Starbucks logo we recognize today. 

The current emblem showcases a simplified and stylized green siren enclosed within a matching green ring, emphasizing a cleaner and more focused visual identity.

Beyond aesthetics, the modern logo carries symbolic weight. The green mermaid within the circle has become an iconic representation of Starbucks’ commitment to delivering high-quality coffee experiences. 

Additionally, it reflects the brand’s emphasis on creating a sense of community that extends beyond geographical boundaries.

In essence, the evolution of the Starbucks logo is a journey from a detailed and intricate design to a streamlined and symbolic representation. It mirrors the brand’s growth, emphasizing its roots, dedication to quality, and the broader cultural impact it seeks to make through coffee and community.

(E) Market Penetration Strategy: How Starbucks became the coffee king?

In this section, we will look into the key plans and actions that helped Starbucks gain a strong foothold in the beverage and cafe industry.

Starbucks continues to blend innovation and growth, navigating the ever-changing landscape of the coffee industry.

(F) Starbucks Entry in India: Core of Starbucks Case Study

In 2012, Starbucks initiated its venture into India through a significant 50:50 joint venture with Tata Consumer Products Ltd. The inaugural flagship store, which opened its doors on October 19th, 2012, found its home in the historic Elphinstone Building in Mumbai. 

The architectural design of this store ingeniously merged Starbucks’ global coffee legacy with the vibrant local culture, creating a welcoming space for community and connection. Over time, this Mumbai location evolved into India’s first Starbucks Reserve® Store, setting the stage for an elevated coffee experience.

(F.1) The Starbucks Reserve® Store Unveiled: A Coffee Lover’s Haven

The introduction of the Starbucks Reserve® Store marked a milestone in the coffee giant’s presence in India. Spanning an impressive 5,200 square feet, this store greeted customers with the intoxicating aroma of coffee. 

The entrance featured a stunning monolithic terrazzo Reserve bar, a masterpiece crafted by local artisans. Trained black apron coffee masters curated an exceptional coffee experience, showcasing rare and exquisite brews through various brewing methods. 

This Reserve Store was not just a coffee shop; it was a canvas for creating unique moments of connection through the artistry of coffee.

(F.2) Expanding Horizons: Tata Starbucks’ Nationwide Presence

Tata Starbucks established a substantial footprint, operating 350+ stores spread across 36 cities in India. In a significant achievement in 2022, Starbucks executed its largest single-year expansion in India, reaching 14 new cities. The brand’s influence spanned major cities such as Mumbai, Delhi NCR, Hyderabad, Chennai, Bengaluru, Pune, and more.

(F.3) Coffee Blends Celebrating Indian Flavors and Heritage

Starbucks paid homage to India’s rich coffee heritage by introducing special blends. The India Estates Blend, sourced from estates in Coorg and Chikmagalur, the birthplace of coffee in India, made its debut in 2013. Additionally, the Diwali Blend, introduced in 2020, served as a tribute to India’s vibrant culture and longstanding coffee traditions.

(F.4) The Tata Alliance: A Successful Partnership

Starbucks in India proudly bore the branding “Starbucks Coffee – A Tata Alliance,” underscoring the synergy between Starbucks and Tata Global Beverages.

Starbucks’ journey in India was not merely about coffee; it was about brewing connections, transcending cultural boundaries, and crafting unforgettable coffee experiences that resonated with the diverse tapestry of India.

(G) Business and Marketing Strategies of Starbucks in India

Starbucks, despite entering India’s coffee scene with strong strategies, faced challenges in a market dominated by competitors like Cafe Coffee Day and Barista Lavazza. Unlike the U.S., where coffee is a staple, India is traditionally a tea-drinking country. 

Starbucks aimed to create a space for relaxation, blending its global coffee legacy with local culture.

Let’s look at the business and marketing strategies of Starbucks in India-

In short, Starbucks’ journey in India reflects a careful blend of global strategies and localized approaches, aiming to create a unique and sustainable presence in a market with diverse preferences and cultural nuances. The success indicators appear promising, showcasing Starbucks’ commitment to long-term growth and meaningful community integration.

Note: Do you know Starbucks collaborated with Apple during the horizontal marketing in the US? We have covered it thoroughly here- Horizontal Marketing System . You can check it out for detailed information.

(H) Wrapping Up the Case Study of Starbucks

The Case Study of Starbucks unveils a fascinating journey that transformed Starbucks into the reigning coffee king. What started as a local coffee bean store in Seattle’s Pike Place Market in 1971 boomed into a global coffee empire. The strategic moves, like Howard Schultz’s visionary shift to espresso-based drinks, had set the stage for Starbucks’ aggressive expansion.

Throughout its evolution, Starbucks faced challenges, leadership changes, and controversies, but resilience and strategic pivots marked its trajectory. The decision to focus on corporate social responsibility under Schultz’s leadership during the financial crisis showcased Starbucks’ adaptability.

The engagement with local cultures, from the iconic two-tailed mermaid symbol to store designs reflecting regional aesthetics, contributed to Starbucks’ success. Key partnerships, like the one with TATA in India, demonstrated a keen understanding of local markets.

Starbucks’ commitment to quality, community, and sustainability resonated with consumers globally. From unique store experiences to tailored product launches, Starbucks consistently adapted its offerings to cater to diverse tastes.

In essence, the Case Study of Starbucks illuminates a narrative of coffee, community, and corporate strategy, culminating in Starbucks’ reign as the coffee king. 

The journey is a testament to the power of adaptability, brand loyalty, and a steaming cup of coffee that transcends borders, making Starbucks an integral part of daily rituals worldwide!

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Contemporary Strategy Analysis, 10th Edition by Robert M. Grant

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Case 9 Starbucks Corporation, March 2018 *

Starbucks Corporation's annual shareholders' meeting on March 22, 2017 marked Starbucks' 25th anniversary as a public company. It also marked a changing of the guard: Howard Schultz, Starbucks founder, CEO, and chairman announced his retirement as CEO and handed over the key of the first Starbucks store to his successor, Starbucks' president and chief operating officer, Kevin Johnson. Johnson was a 16‐year Microsoft veteran who had been CEO of Juniper Networks before joining Starbucks.

Stepping in Schulz's “venti‐sized” shoes presented a massive challenge to Johnson and for his first year as CEO, he kept a low profile. When Starbucks hit turbulence—as in April when two African‐American men were arrested at a Starbucks in Philadelphia—it was Schultz who was the public face of the company. However, in June 2018, Schulz also announced his retirement as executive chairman of Starbucks—fueling speculation that he was planning to run for president of the United States of America as a Democratic candidate.

Johnson was now the official and de facto leader of Starbucks Corporation. As he acknowledged: “The most difficult transition any company will ever go through is from founder‐led to founder‐inspired.” 1 Moreover, Starbucks was facing significant strategic and operational challenges. Some of Starbucks' diversification—into tea shops for example—had been unsuccessful, and in the United States, same‐store sales growth had declined, causing the ...

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Starbucks Commits to Raising Awareness of Racial Bias

Brian Kenny: If you happen to be driving on I-96 just outside of Detroit, there's a billboard that will surely get your attention. It reads, “Driving while Black, racial profiling just ahead, welcome.”

“Driving while Black” is an all too familiar term in the US and it highlights one of the many indignities that Black people endure on a daily basis as a result of implicit or explicit bias. In fact, almost any activity that seems mundane to whites, becomes stressful and anxiety-ridden when you do it while Black. Shopping, studying, parenting, and of course, dining. It's a documented phenomenon, the journal of Black studies surveyed 200 restaurant servers in North Carolina and found that 38.5 percent admit to discriminating against Black customers while 59 percent say they've witnessed discrimination by others. Meanwhile, Black diners report that they are often mistaken for valets, coat checks, and washroom attendance. And it's not just a Southern thing. Today's case takes us to a Starbucks in Philadelphia where two Black men seated at a table, waiting for a friend, would become the latest examples of the perils of dining while Black. Today on Cold Call, we'll discuss the case study entitled, Starbucks: Reaffirming Commitment to the Third Place Ideal , with coauthors, Francesca Gino and Katie Coffman. I'm your host, Brian Kenny, and you're listening to Cold Call. Brought to you by Harvard Business School.

Francesca Gino: Thank you for having us, Brian.

Katie Coffman: Great to be here.

Brian Kenny: And we are socially distanced, in fact, we are more than socially distanced, we're far away from each other because we are continuing here to be in the midst of the pandemic. And we are in the midst as well of another major crisis unfolding in the United States. And that has to do with George Floyd's death and the Black Lives Matter movement. So this case to me feels all the more timely and prescient. And ironically, we started talking about doing this case on the podcast well, before that happened. So I think a lot of our questions and conversation today will reflect on the current situation and what we can learn from this case. Katie, I'm going to start off with you; could you just set the scenario, tell us what happened on April 12th, 2018.

Katie Coffman: I think it's a scene we've probably all seen in a Starbucks at one point in our life or another, which is two individuals sitting at a table waiting for another person to arrive. But in this scenario, those two individuals were African American men. And the situation was, these were two aspiring entrepreneurs waiting for a business associate that they were going to have a meeting with. They sat down to wait, they hadn't purchased anything. One of them asked to use the restroom and an employee responded that because they hadn't purchased anything, the restrooms were for customers only. After that they sat back down and the employee approached them again and told them, can I help you with something? Sort of asking, what are you doing here? And they said, "We're waiting for a meeting." And that employee asked them to make a purchase or leave. Within minutes of that encounter with no escalation of voices or any conflict beyond that, the police had been called. And the police arrived, there was a confrontation pretty shortly thereafter and just two police officers turned into six police officers. The men were told they had one more chance to leave, even though other customers, the business associate showed up and said, "We think these people are being treated unfairly, this doesn't seem like they've done anything wrong." The police still stayed and told the men they were actually no longer free to leave. And they were brought to a local station in handcuffs and placed in a cell for several hours before ultimately being released that evening with no charges filed.

Brian Kenny: So that sounds just so familiar, doesn't it? In the current context, and we know that it could have even ended much more tragically than it did, but it's amazing the parallels to what we saw happen with George Floyd and then in so many other instances. Francesca, you've taught it in the classroom. I'm wondering if you could just tell our listeners, how do you dive into this conversation? What's your cold call in this particular case?

Francesca Gino: The first question I always love to ask in class is a question that gets us to analyze how Starbucks responded to the Philadelphia incident. And so I would ask students right off the bat, what were the features of Starbucks response? What were some of the strengths and some of the weaknesses in the response that they used? I did try a couple of time to go down a different route. And maybe this is an approach that is a little bit more courageous because it can bring out right at the start of class, a lot of emotions. But basically I set the stage by telling students why we're discussing the case, that we're really interested in trying to evaluate how this big organization has reacted to an incident of discrimination directly in its store. And an incident, let's not forget, that was caught on camera by customers and it basically became viral within hours. And so I basically say, look, this is obviously not a case in isolation. I ask people to reflect on their experiences for a moment and then ask them whether they would be willing to share the experience, or at least to tell us the words that they would use to describe how they felt. And I described it as a more courageous route, but I think it's a discussion that might be important because it gives students really an opportunity to engage with the challenges of unconscious bias and how so easily this, maybe even without a bad intention, can turn into discriminatory behavior.

Brian Kenny: So we're going to talk a lot about unconscious bias in the course of this conversation, some of our listeners might be thinking, this is a business focused podcast, what does this have to do with business? So I guess I would ask both of you the next question. Why did you decide to write the case? How does it relate back to the kinds of things that you look at as a scholar?

Francesca Gino: Maybe I'll get started by telling you why I think this is a case that it's quite important for a business school and for an audience, whether it's executives or MBA students. Through the case, we basically get to evaluate how a very large organization that operates globally and really takes pride in being open, inclusive in its culture and being a space that is between home and work, reacted to an incident where an employee used discriminatory behavior. And to me, especially in a world where, because of social media, what we do inside organizations becomes knowledge that a lot of people around the globe can get access to. And so they get to see everything that is happening in the moment. It's really important to think through how would we react as leaders or how is it that we're creating the conditions intentionally or not to see people in our own organizations react in this way?

Katie Coffman: There's obviously the moral and ethical imperative to try and root out racism in all forms in our organizations. But on top of that, I think there's increasing awareness of the missed business opportunity. If your organization, especially in the customer service type industry, is not a place that's consistently welcoming, friendly, fair, you're not going to be as successful as a business. So I think understanding the ways where we could actually reach a broader audience with our products, with our services across a variety of industries, is really important and can be a missed opportunity for a lot of organizations.

Brian Kenny: So we know there are significant costs to the brand of firms that find themselves in these kinds of situations. Right? But there are other costs I would imagine that are associated with it, too. Katie, I wonder if you could talk a little bit about some of the, both, I guess the level of pervasiveness of bias, whether it's unconscious or not. And I do want to talk a little bit about what that means, but what are the ripple effects of these kinds of things in terms of the cost of doing business?

Katie Coffman: You could think both about the cost among your own employees and the culture within your organization, the extent to which your employees really feel aligned with the mission and values of your organization, how effectively they're able to work with each other and bring their whole selves to work. So you have to think about, I think the employee side of things, and on the flip side for a company, particularly like Starbucks, you have to think about the customer side of things, which is what's our relationship with the community? What's our relationship with our patrons? Are we a place that people feel comfortable going to, feel proud of going to and include as part of their daily routine? I know in our conversations with Starbucks, a number of members of their leadership team talked about how easy it is to lose a potential customer through one bad experience. And certainly a bad experience like this, has the capacity to reach a large number of people, not just the specific individual who was targeted. In constructing the employee culture you want, but also in making sure that you're an appealing place for people to shop, to do business, to spend their money. Making sure that your organization is as free from the negative consequences of bias as possible. It is really a business imperative.

Brian Kenny: Obviously, businesses have been thinking a lot about this, right? This is not a new phenomenon. I think we've talked about finding ways to make people aware of their bias so that they are more thoughtful about how they're engaging with customers and with coworkers and such. Francesca, I would ask you, how persistent is this problem? Have we made any headway or is it just as bad as it's always been?

Francesca Gino: So I would coach this in a couple of observations. First of all, one of the ways in which I believe we've made progress and organizations and leaders have made progress is by focusing more often or more attentively to creating work places that are inclusive. In fact, I would say it's hard to think of leaders who don't think that diversity and inclusion are important to their organizations. Where I don't see a lot of progress yet is truly understanding what it takes to be an inclusive leader or to create an inclusive environment. And it's sometimes troubling to leaders and to employees alike to realize that some of these biases happen at the unconscious level. And to realize that our human nature is imperfect. What I think is equally surprising is to think about what can effectively drive change. So there have been a lot of organizations doing unconscious bias training, often not with the results that they expected. And I think that comes down to not truly understanding what solutions are helpful to reducing unconscious bias. The organizations that are making headway or that are being more thoughtful are really organizations that do not think about diversity inclusion as a HR problem. But somebody or leaders who thinks that fundamentally we need to make, I would say inclusion and diversity part of the DNA of the organization. And that requires much more thoughtfulness and requires being willing of being part of a messy journey where you might not get everything right.

Brian Kenny: But Starbucks had that, Howard Schultz had a vision for Starbucks and they focused a lot on this. Didn't they, Katie? Wasn't this something that was important to Starbucks and apparently it didn't take?

Katie Coffman: I think that's one of the really important learnings from this case, because if we look at the history of Starbucks and their mission and values, one of the fundamental principles they had is that Starbucks is going to exist as what they would call a “third place” for their communities. And the idea of a third place is, you have your home, you have their work, Starbucks is going to be a third place where you could really just be. Right. It's welcoming, it's safe, it's inclusive. You can come, you can have a cup of coffee, you can do your work and it's going to feel like one of those safe, special places to you. And that's been a part of their value systems really from the start. And yet even with that type of mindset and a set of policies aimed at achieving that, you can still have this kind of behavior and this kind of outcome. And I think you could make the same analogy when thinking about individuals. Even individuals with really good intentions, who would not view themselves as a racist person can still, because of these unconscious biases end up with actions, with behaviors, with poorly chosen words, doing things to create problems, particularly for underrepresented groups. And so I think part of the recognition here is this really can happen to any organization, to any person. And you have to have a much deeper understanding of the root causes of these types of behaviors and what we can actually do to make those unconscious biases less problematic in our lives and in our workplaces.

Brian Kenny: So to your point a Francesca, this can't just be an HR initiative, right? This has to be an initiative that cuts across the fabric of the entire organization.

Francesca Gino: Exactly. And one of the things I do appreciate in the response to the Philadelphia incident is that Sternberg started a journey. So everybody, especially the press has been very focused on the fact that they ended up closing down their stores on a particular day to do some training. That was about understanding racial injustice and also understanding unconscious bias. But it was really the beginning of a much larger journey. And so it's interesting that maybe that closing the stores was symbolic to get us started, but it was one only one step of a journey that is still continuous. And again, I think that the leaders were going to be really thoughtful about this need… to be ready to take steps that might not be the right ones, but at least to try their best to address issues that might be happening across all parts of the organization. And it's both about the behavior of the leaders and the employees who work in the organizations, but also trying to understand whether there are policies or systems that are becoming, system that systematically reinforce discriminatory behavior potentially. Like in the case of Starbucks, the policies that they introduced at some point about who's a customer and who's not a customer, might in fact, have contributed to, by judgment on the part of the store manager.

Katie Coffman: I completely agree with what Francesca said and I think so much attention has been paid to that May 29th store closure. In my opinion, one of the smartest things they did were these policy changes, right? So really shortly after this incident, they recognized that their policies were maybe the biggest contributing factor to this incident. And in particular, putting the owners and store managers to make distinctions between customers and non-customers in terms of what was going to be permitted in terms of bathroom use. And they gave them much more explicit guidance on what type of behaviors are appropriate and not appropriate in the store and gave them an explicit guide for actually addressing disruptive behavior. That type of de-escalation emphasis and giving that to their employees, their store managers, their partners, so that they were much better position from a policy perspective to actually handle these types of incidents in a much better way.

Brian Kenny: So let's dive into what Starbucks did, because I think that's the basis of a lot of the most salient points in the case. They shut everything down, did they mandate for employees to go to this training? Was it optional or did you have to do it?

Francesca Gino: They did not make it mandatory. So they allowed people to choose and they didn't actually record whether or not you participated. But from the qualitative data that they collected, it seems as if most people were actually there taking the course and the training.

Brian Kenny: And what was the training like? What were they trying to teach?

Katie Coffman: It's incredibly impressive from an operational perspective, both how quickly and how comprehensively they were able to put together this program. So I think the idea was to spend a couple of hours with store managers, store partners, increasing awareness, and doing education around racial bias. And to produce that content, they actually worked with a variety of both internal and external resources, really consulted with experts in this area to try and make that training as impactful as possible. You have 8,000 stores, there's no way you're going to be able to recruit and train and deploy a bias training facilitator for each of those stores, so they created this centralized version of the content that could then be deployed with iPads, accompanied with a guidebook to help store managers actually navigate and lead the discussion within their own stores. They also gave private notebooks to all the employees who would participate so they could answer questions privately, make reflections, and actually take those home with them with hopes that these learnings would last a little bit longer.

Brian Kenny: And were the managers leading their own teams in this exercise? I mean, was it like a cascading type effect?

Francesca Gino: Exactly. So the store manager is in charge of leading the training, paced according to how the people in the store are actually reacting to the content.

Katie Coffman : I'll just add to that too. I think an important part of their approach, and we mentioned this in the case is that all of this is also paid time. Right. And I think that says something about the mindset too. This isn't extracurricular activity that, hey, wouldn't it be great if we got a little bit better on this, maybe you should spend some time with this. This is employees being paid to engage in these important conversations. And I think that sends a really key message to everyone in the organization.

Brian Kenny: So did it work and how did employees respond to it?

Francesca Gino: So measuring success is actually a question that we ask in class when we teach this case, because we're interested and intend to understand how do you know after making such big investments that you're making progress? That in fact you are looking at your employees, you're looking at your leaders and you see that they're treating each other equally, that there is no sign of discrimination. And this is a really hard question to ask, not only to the students in class, but also when we asked it to the leaders themselves. A couple of data points that they brought up that were interesting: first is what seems to be the climate in the stores around actually having these conversations. And the fact that again, the people in the store often mirror the type of communities that they serve made people feel more included because they had more opportunity to talk about their experiences. Or if you look specifically at the store in Philadelphia, that actually had the incident. If you look at it from a profitability standpoint, the year after the incident was quite a good year for them. And so there are elements, maybe not perfect data or the type of objective performance measures that many leaders may like or that Katie and I would love to see when we look at our research and try to understand the effect of a policy change, but there are at least indicators that can help us understand whether this has been successful or not. And if I were to put my scholar's hat on or my instructor's hat on, I would also try to understand if the journey they've been on fundamentally touches on some of the elements that we know are important when we're trying to fight unconscious bias and discriminations. So for example, the presence of information that is counter stereotypical, or trying to increase content or connecting with empathy, with people who look different from you. And so if I keep this element in mind then I would say, I think that they're on a good standing, given the type of conversations that they've started.

Brian Kenny: Katie, let me ask you this question. I'm wondering in the wake of what happened with George Floyd and what feels like a movement at this point, something feels different about what's happening in our country right now. Does this change the inflection of the conversation that you would see yourself having with students around this?

Katie Coffman: One of the things I've taken away from the last couple of weeks, or maybe now months of this social movement, is that the bar for what it means to be a good person in this context is going up. And I think going up in a really good way. It's no longer enough to be someone who's not actively engaging in discrimination. It's now important to actually, I think the term we're seeing a lot is to be anti-racist, right. That you're taking active actions to make the systems, the institutions, the culture around you, one that is less racist and more equitable. When you think about judging Starbucks's response, I think in a lot of ways over the last year now, the bar is going up for whether Starbucks's response is good enough. And I think that's actually a really positive reflection on where we're moving from a societal perspective, which is, we're not just getting rid of problems, but are they actually doing enough to create change? And so I'm really excited to teach this case more in the coming year and hear from our students and from executives of how they see the recent conversations, changing their views of this incident and the response. I think it couldn't be more timely.

Francesca Gino: I also think that there is an aspect of the case that usually doesn't get as much attention or didn't get as much attention in previous sessions that I've taught when I use this case. We focus a lot on how you would react as a leader if you were actually to see something happening like this in your own organizations. So the attention is on the store managers calling the police on these two Black customers. But the story is a little bit more complex and richer. How about the police officers who were there and arrested the two individuals without too many questions about what had happened? And so I think given the current social crisis that we're living through, I think the students, whether MBAs or executives, would point to the role of the officers much more quickly and likely they would also point to the customers who were sitting there watching this unfold and practically doing anything.

Katie Coffman: And there, I would applaud Starbucks too, because in thinking about their response and maybe it didn't stand out at the time, but I can tell you reading back through it now, a lot of the guidance they provided is, who can you call before you call the police? If there's a behavior that's going on that you think needs to be addressed, where can you turn first? Should you actually be turning attention to a mental health professional? Is there a shelter that you need to be connecting with? And all of these other resources. And I think it's just so very connected to the conversations we're having now, the movement to defund the police, that wasn't really part of the mainstream conversation at the time of the incident. And yet I think Starbucks had a lot of foresight to say, if we can avoid the stage where we're even calling the police in the first place, maybe that has the potential to have a real positive impact.

Brian Kenny: One last question for each of you, and this has been really interesting hearing you talk about the case. I think lots of great insights here. But for our listeners, almost all of whom I think are people who are interested in business or practicing business in some way, some are leaders, some are managers, some are somewhere in between. What can they do individually? Because I think a lot of us look at this and it's like boiling the ocean, it seems like such an enormous problem. And what kind of impact could I have? But I would expect that there are some things that people can do practically within their role, no matter what they are. So Katie, I'll ask you to maybe comment on that, what piece of advice you'd like to give and then Francesca, same from you.

Katie Coffman: I think awareness of unconscious bias is an important first step and recognizing the prevalence of it in ourselves and across others that we're going to encounter. And I think that type of awareness is an important first step, but hardly sufficient. And so when do these unconscious biases become most problematic? Well, it's really about these implicit associations, these intuitive decisions we make, these quick decisions we make. So both in our policy design and in our individual actions, can we interrupt that mapping from unconscious bias, implicit association to action. Because it's going to be very hard, I think we've seen in research, you can't just suddenly become someone who has no unconscious bias. But I think the case is much stronger that you can become someone who takes actions and acts with a deliberate hand to make it the case that whatever unconscious bias you have is going to be less likely to impact the decisions you make.

Brian Kenny: Great. Francesca?

Francesca Gino: I was struck in writing this case and talking to the leaders about the reaction that the CEO had, Kevin Johnson, when he was thinking back to the moment he realized this had happened, and it did seem it was discriminatory behavior. And he kept asking himself two questions. One was, what is the right thing to do? How did we prepare our people for this? And I think that that is a very profound question because unconscious bias is in fact pervasive. And as leader, we do play a role in trying to understand it better but also give people the opportunity to have conversations, to try to understand how to pause in the moment and make sure that they're coming up with the right judgment and the right behavior in that particular moment.

Brian Kenny: Great advice. And hopefully there will be other cases forthcoming where companies are showing great examples of how to do this and to succeed at it. Katie and Francesca, thank you so much for joining us on Cold Call.

Francesca Gino: Thank you so much, Brian, for having us.

Katie Coffman: Thank you so much.

Brian Kenny: If you enjoy Cold Call, you might like other podcasts on the HBR Presents network. Whether you're looking for advice on navigating your career, you want the latest thinking in business and management, or you just want to hear what's on the mind of Harvard Business School professors, the HBR Presents network has a podcast for you. Find them on Apple Podcasts or wherever you listen. I'm your host, Brain Kenny, and you've been listening to Cold Call, an official podcast of Harvard Business School on the HBR Presents network.

Brian Kenny is Chief Marketing and Communications Officer at Harvard Business School.

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Starbucks C.E.O. Apologizes After Arrests of 2 Black Men

By Matt Stevens

• April 15, 2018

Two black men walked into a Starbucks in downtown Philadelphia on Thursday afternoon and sat down. Officials said they had asked to use the restroom but because they had not bought anything, an employee refused the request. They were eventually asked to leave, and when they declined, an employee called the police.

Some of what happened next was recorded in a video that has been viewed more than eight million times on Twitter and was described by the chief executive of Starbucks as “very hard to watch.” Details of the episode, which the authorities provided on Saturday, ignited widespread criticism on social media, incited anger among public officials and prompted investigations.

The video shows the men surrounded by several police officers wearing bicycle helmets in the Center City Starbucks. When one officer asks another man whether he is “with these gentlemen,” the man says he is and calls the episode ridiculous.

“What did they get called for?” asks the man, Andrew Yaffe, who is white, referring to the police. “Because there are two black guys sitting here meeting me?”

Moments later, officers escort one of the black men out of the Starbucks in handcuffs. The other soon follows.

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Two black men were arrested in a Philadelphia Starbucks for doing nothing

They were there for a business meeting. Starbucks says it’s sorry. The police chief says officers did nothing wrong.

by Emily Stewart

Two black men were arrested and escorted out of a Philadelphia Starbucks on Thursday after staff called the police to report they refused to leave; the men hadn’t ordered anything and were reportedly waiting for a business associate to arrive. The staff reportedly called 911 because Starbucks does “not allow nonpaying people from the public to come in and use the restroom,” Philadelphia Police Commissioner Richard Ross told the Philadelphia Inquirer . The employees said the men were trespassing and had refused to leave the restaurant.

A video of the incident has swept across the internet and sparked widespread outrage, prompting Starbucks to issue a less-than-satisfying apology on Saturday afternoon. CEO Kevin Johnson issued a lengthy statement on the incident on Saturday evening and said he wants to meet personally with the men arrested to apologize.

The saga began when a video posted on Twitter on Tuesday showed police arresting two black men in Twitter for “doing nothing,” in the words of the user who posted the video. Two people — not the men — can be heard protesting as the police remove the men’s chairs and escort them out. “This is ridiculous,” one white man says to an officer in the video. The men do not protest.

The video has been viewed more than three and a half million times on Twitter since Thursday.

Police responded to the call and to keep things from “getting out of hand,” he said, and asked the men to leave, as Starbucks did not want them there. Ross defended the officers, saying they “ did absolutely nothing wrong, ” but the police department said it was conducting an “internal investigation.”

There are a lot of questions here, and they’re not just about the police.

The video of the incident shows at least six police officers taking the two men into custody — a high number, given they were doing nothing.

The obvious question: Beyond the police’s response, why were they called in the first place? People meet in Starbucks all the time, and they wait for others in the restaurant before ordering. Starbucks issued an apology on Saturday to the “two individuals and our customers” and said the company is “disappointed” that it led to an arrest. “We are reviewing our policies and will continue to engage with the community and the police department to try to ensure these types of situation never happen in any of our stores,” the statement says.

Johnson in a longer statement released on Saturday evening reiterated the apology and said the company plans to investigate the incident and “make any necessary changes to our practices that would help prevent such an occurrence from ever happening again.” He said Starbucks is “firmly against discrimination or racial profiling” and that he hopes to meet the men “to offer a face-to-face apology.”

A second video from the Thursday incident posted on YouTube shows an extended version of what happened. The two black men who are ultimately arrested speak calmly to police. A third man, later identified real estate developer Andrew Yaffe, who is white, appears and protests.

“Does anybody else think this is ridiculous?” he asks, calling it “absolute discrimination.” Yaffee spoke with attorney Lauren Wimmer about the incident, and she talked to the Philadelphia Inquirer . “He was meeting with the two gentlemen at the Starbucks to discuss business,” Wimmer said Saturday, identifying Yaffe as a friend. “These two guys are business professionals in commercial real estate.”

Wimmer is representing the men who were arrested, who have not been identified publicly. She told the Inquirer she believes the reason for the arrest was “completely based on race” and noted there was “no indication any crime was being committed.”

Starbucks is not the first major restaurant chain to come under fire for racial discrimination. In 1994, Denny’s agreed to pay more than $54 million to settle racial discrimination lawsuits; in 2004, Cracker Barrel paid $8.7 million in discrimination lawsuits.

Update: Story updated with statement from Starbucks CEO Kevin Johnson.

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Meet Me at Starbucks

Two black men were arrested after an employee called the police on them, prompting Starbucks to implement “racial-bias” training across all its stores.

On April 12, 2018, at a Starbucks location in Philadelphia, two black men, Rashon Nelson and Donte Robinson, were waiting for a friend, Andrew Yaffe. Nelson and Robinson were entrepreneurs and were going to discuss business investment opportunities with Yaffe, a white real estate developer. As they waited, an employee asked if she could help them. They said “no,” that they were just waiting for a business meeting. Then a manager told Nelson that he couldn’t use the restroom because he was not a paying customer.

Because the two men had not purchased anything yet, a store manager called police, even though Robinson had been a customer at the store for almost a decade and both men had used the store location for business meetings before. At least six Philadelphia Police Department officers arrived. The police officers did not ask the men any questions; they just demanded that they leave immediately. They declined. The police officers then proceeded to arrest the men for trespassing. As the arrest occurred, Mr. Yaffe arrived. Seeing what was happening, Yaffe said:

“Why would they be asked to leave? Does anyone else think this is ridiculous? It’s absolute discrimination.”

The two men were taken out in handcuffs. They were taken to the police station, photographed, and fingerprinted. They were held for almost nine hours before being released from custody. Prosecutors decided that there was insufficient evidence to charge the men with a crime.

After a video of the arrest went viral, Starbucks CEO Kevin Johnson released a statement:

“We apologize to the two individuals and our customers and are disappointed this led to an arrest. We take these matters seriously and clearly have more work to do when it comes to how we handle incidents in our stores. We are reviewing our policies and will continue to engage with the community and the police department to try to ensure these types of situations never happen in any of our stores.”

Johnson then announced that every company-owned Starbucks location in the nation would close on May 29, 2018, for “racial-bias education.” When one customer complained on Facebook that closing the stores because of just one incident seemed overkill, Starbucks responded:

“There are countless examples of implicit bias resulting in discrimination against people of color, both in and outside our stores. Addressing bias is crucial in ensuring that all our customers feel safe and welcome in our stores.”

A similar complaint about closing thousands of stores because of the actions of a handful of employees prompted this response from Starbucks: “Our goal is to make our stores a safe and welcoming place for everyone, and we have failed. This training is crucial in making sure we meet our goal.”

Discussion Questions

1. Do you think the manager of the Starbucks in Philadelphia thought of herself as racist?

2. Do you think that what happened to Nelson and Robinson would have happened had they been white?

3. What stereotypes were invoked in this case and by whom?

4. How did stereotyping influence and/or frame the situation for the manager? For the police? For bystanders?

5. What is your opinion about Starbucks’ response to the arrest of Nelson and Robinson?

6. Will Starbucks’ training session on implicit bias have a beneficial impact?

Related Videos

Implicit Bias

Implicit bias exists when people unconsciously hold attitudes toward others or associate stereotypes with them.

Bibliography

“Starbucks CEO Apologizes After Employee Calls Police on Black Men Waiting at a Table,” https://www.washingtonpost.com/news/business/wp/2018/04/14/starbucks-apologizes-after-employee-calls-police-on-black-men-waiting-at-a-table/?noredirect=on&utm_term=.58c312135cfa

“Starbucks to Angry Facebookers: We Can’t Deny This is a Race Issue,” https://www.fastcompany.com/40561997/starbucks-to-angry-facebookers-we-cant-deny-this-is-a-race-issue

“Black Men Arrested at Philadelphia Starbucks Say They Feared for Their Lives,” https://www.cbsnews.com/news/starbucks-arrest-rashon-nelson-donte-robinson-feared-for-their-lives/

“Is This How Discrimination Ends?” https://www.theatlantic.com/science/archive/2017/05/unconscious-bias-training/525405/

“Does Starbucks Understand the Science of Racial Bias?” https://www.theatlantic.com/science/archive/2018/05/starbucks-unconscious-bias-training/559415/

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Please note you do not have access to teaching notes, starbucks: global brand in emerging markets.

Publication date: 4 December 2018

Teaching notes

Learning outcomes.

Students after reading the case will learn about the issues and challenges of expansion in emerging markets. Global expansion versus multinational expansion. Stardardization versus localization. Socio-cultural aspects in international marketing. Leadership succession in multinational companies.

Case overview/synopsis

The case is about Starbucks’ journey of global expansion. It focuses on challenges in emerging markets. It also talks about the challenges to new CEO Kevin Johnson post stepping down of iconic leader Howard Schultz.

Complexity academic level

MBA Executive MBA Specialisation in Strategy, International Marketing.

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Subject code

CSS 5: International Business.

• Competitive strategy
• Customer relationship management
• Global marketing strategy
• International market entry

Acknowledgements

Disclaimer. This case is written solely for educational purposes and is not intended to represent successful or unsuccessful managerial decision-making. The authors may have disguised names; financial and other recognizable information to protect confidentiality.

Gupta, P. , Nagpal, A. and Malik, D. (2018), "Starbucks: global brand in emerging markets", , Vol. 8 No. 4. https://doi.org/10.1108/EEMCS-03-2018-0044

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Copyright © 2018, Emerald Publishing Limited

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Starbucks sued for allegedly using coffee from farms with rights abuses while touting its ‘ethical’ sourcing

A consumer advocacy group is suing Starbucks, the world’s largest coffee brand, for false advertising, alleging that it sources coffee and tea from farms with human rights and labor abuses, while touting its commitment to ethical sourcing.

The case, filed in a Washington, D.C., court on Wednesday on behalf of American consumers, alleges that the coffee giant is misleading the public by widely marketing its “100% ethical” sourcing commitment on its coffee and tea products, when it knowingly sources from suppliers with “documented, severe human rights and labor abuses.”

“On every bag of coffee and box of K-cups that Starbucks sells, Starbucks is heralding its commitment to 100% ethical sourcing,” said Sally Greenberg, CEO of the National Consumers League, the legal advocacy group bringing the case. “But it’s pretty clear that there are significant human rights and labor abuses across Starbucks’ supply chain.”

The lawsuit cites reporting about human rights and labor abuses on specific coffee and tea farms in  Guatemala ,  Kenya  and  Brazil , and alleges that Starbucks has continued to purchase from these suppliers in spite of the documented violations.

A spokesperson for Starbucks did not immediately respond to a request for comment on its sourcing relationships with the farms and companies mentioned in the lawsuit, but in an earlier statement to NBC News said, “We take allegations like these extremely seriously and are actively engaged with farms to ensure they adhere to our standards. Each supply chain is required to undergo reverification regularly and we remain committed to working with our business partners to meet the expectations detailed in our  Global Human Rights Statement .”

In Brazil, labor officials have cracked down on several  reported  Starbucks suppliers over abusive and unsafe labor practices in recent years, including garnishing the cost of harvesting equipment from farm workers wages, not providing clean drinking water, personal protective equipment and bathrooms, and employing underaged workers. In 2022, 17 workers, including three minors, were rescued by Brazilian inspectors from “modern slavery,” according to  Reporter Brasil , at a coffee farm managed by a man whose coffee roaster company received Starbucks’ seal of certification a month earlier.

In response to the Reporter Brasil stories and reported labor abuses in Kenya and Guatemala cited in the lawsuit, Starbucks issued statements at the time that the company was “deeply concerned,” and that it would “thoroughly investigate” claims of labor violations, “take immediate action” to suspend purchases or “ensure corrective action” occurred.

In a promotional video on its  coffee academy  website, a Starbucks coffee buyer says the company’s ethical sourcing stamp “means that we are buying coffee, making sure that it’s good for the planet and good for the people who produce it.”

Greenberg said the suit aims to prevent Starbucks from making claims like those — particularly its “Committed to 100% Ethical Coffee Sourcing” advertising — unless the company improves labor practices within its supply chain.

Starbucks, like many companies, uses third-party certification programs to ensure the integrity of its supply chains for tea and cocoa. The company launched its own sourcing standards, called C.A.F.E. Practices, in 2004 to oversee its coffee sourcing in more than 30 countries. The verification program is administered by a company called SCS Global Services in collaboration with Conservation International.

The verification program holds Starbucks coffee suppliers to more than 200 environmental, labor and quality standards. Farms that fail to meet those can be barred from supplying the company until corrective action is confirmed.

But there have long been issues with how effective such programs are, according to experts.

In 2021, Rainforest Alliance, the third-party that certifies Starbucks’ supply chains for tea and cocoa, was sued in D.C. court by another consumer advocacy group over “false and deceptive marketing” of Hershey’s cocoa as “100 percent certified and sustainable.” A judge ruled last year that the case could move forward only against Hershey, as the manufacturer of the products. 

Rainforest Alliance did not immediately respond to a request for comment. 

“There is this huge pile of evidence that shows that the mechanisms that [certifiers are] relying on to address problems like forced labor, child labor, gender based violence, are extremely flawed and not working very well,” said Genevieve LeBaron, director of the School of Public Policy at Canada’s Simon Fraser University.

“We have incident after incident that’s uncovered in these supply chains. And still, companies go around and make these kinds of claims that they have 100% sustainable or ethical sourcing” said LeBaron, whose  research  into cocoa and tea has shown that the prevalence and severity of labor violations on certified and uncertified farms was “basically identical.”

LeBaron, who has consulted for the United Nations on global supply chain ethics, said the issue is not unique to Starbucks, but ethical commitments from large purchasing players like Starbucks can have an outsize impact on the integrity of supply chains if they are backed up.

Starbucks has 10 “farmer support  centers ” in coffee-producing regions around the globe, including Brazil and Guatemala, but does not release public lists of certified suppliers, making it difficult to track how often its suppliers are found to be engaging in labor abuses.

“I think it is really hard to have an ethical supply chain. And I would say, you know, a lot of the reason for that is that, especially in agriculture, there’s a sort of status quo of sourcing goods way below the cost of actually producing them. And as long as you have that, you’re gonna have problems,” LeBaron said.

This story first appeared on NBCNews.com .

Adiel Kaplan is a reporter with the NBC News Investigative Unit.

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Urban Flood Risk Management Through the Lens of Citizen Science: A Case Study in Dhaka

42 Pages Posted: 4 Sep 2024

Farzana Ahmed Mohuya

University of Dhaka

Claire L. Walsh

Newcastle University

Hayley J Fowler

Newcastle University - School of Engineering

Dhaka is one of the most densely populated cities in South Asia. In recent times, prolonged urban flooding/water logging is a recurring phenomenon and major concern in the two city corporations’ areas. This study investigates how “citizen science” could help individuals, communities, and stakeholders to understand and manage the risk of current and future urban flooding, by integrating people’s experience, concerns, and opinions on flood risk management into the formal framework. A questionnaire survey was conducted among 500 respondents in water logging affected wards of Dhaka. We identified that every year respondents in two city corporations experience 1-3 days of water logging, mostly during the monsoon season. Respondents were found to be aware about flooding and its associated risks and emphasised a concern about the increasing frequency of urban flooding in Dhaka over the next 10 years. Although 61.2% of the respondents were not familiar with the concept of citizen science, 42.8% of respondents expressed eagerness to become involved in any related project to promote awareness and mitigation of urban flooding issues in their communities. Key stakeholder and focus group discussions exposed that unplanned urbanisation, poor drainage system management, inappropriate waste management systems, and recent extreme rainfall events are the major perceived drivers behind urban flooding in Dhaka. Our discussions emphasised the need for integration of both modelling and geospatial techniques to build a Volunteer Geographic Information (VGI) system for the mitigation. We conclude that citizen science approach could play a significant role in tackling urban flooding risks in Dhaka.

Keywords: Urban Flooding, Flood risk, Risk perception, Citizen science, Dhaka

Suggested Citation: Suggested Citation

Farzana Ahmed Mohuya (Contact Author)

University of dhaka ( email ).

University of Dhaka Dhaka 1000 Ramna, Dhaka, 1000 Bangladesh

Newcastle University ( email )

Newcastle upon Tyne NE1 7RU United Kingdom

Hayley Fowler

Newcastle university - school of engineering ( email ), do you have a job opening that you would like to promote on ssrn, paper statistics, related ejournals, environmental geoscience ejournal.

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DIAMANTE: A data-centric semantic segmentation approach to map tree dieback induced by bark beetle infestations via satellite images

• Open access
• Published: 04 September 2024

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• Giuseppina Andresini 1 , 2 ,
• Annalisa Appice 1 , 2 ,
• Dino Ienco 3 , 4 &
• Vito Recchia 1  

Forest tree dieback inventory has a crucial role in improving forest management strategies. This inventory is traditionally performed by forests through laborious and time-consuming human assessment of individual trees. On the other hand, the large amount of Earth satellite data that are publicly available with the Copernicus program and can be processed through advanced deep learning techniques has recently been established as an alternative to field surveys for forest tree dieback tasks. However, to realize its full potential, deep learning requires a deep understanding of satellite data since the data collection and preparation steps are essential as the model development step. In this study, we explore the performance of a data-centric semantic segmentation approach to detect forest tree dieback events due to bark beetle infestation in satellite images. The proposed approach prepares a multisensor data set collected using both the SAR Sentinel-1 sensor and the optical Sentinel-2 sensor and uses this dataset to train a multisensor semantic segmentation model. The evaluation shows the effectiveness of the proposed approach in a real inventory case study that regards non-overlapping forest scenes from the Northeast of France acquired in October 2018. The selected scenes host bark beetle infestation hotspots of different sizes, which originate from the mass reproduction of the bark beetle in the 2018 infestation.

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1 Introduction

Forests and woodlands cover roughly one-third of Earth’s surface and play a critical role in providing many ecosystem services, including carbon sequestration, water flow regulation, timber production, soil protection, and biodiversity conservation. However, the accelerating pace of climate change and its impact on species distribution and biome composition are leading to an increase in various types of disturbances, whether biotic, abiotic, or a combination of both, which are now affecting this vital natural resource and resulting in forest loss. Consequently, the decline in key forest ecosystem services is becoming more and more apparent. Among all the disturbances, insect infestations and disease outbreaks (e.g., bark beetle infestations) can induce massive tree dieback and, subsequently, significantly disrupt ecosystem dynamics (Gomez et al., 2023 ). This is why forest surveillance is crucial to monitor, quantify and possibly prevent outbreak diseases and enable foresters to perform informed decision-making for effective environmental management. Nevertheless, common strategies used to evaluate the health of forested regions primarily rely on laborious and time-consuming field surveys (Bárta et al., 2021 ). Consequently, they are restricted in their ability to cover extensive geographical areas, thereby preventing large-scale analysis across vast territories. To this end, the substantial amount of remote sensing information collected today via modern Earth observation missions constitutes an unprecedented opportunity to scale up forest dieback assessment and surveillance over large areas. As an exemplar, the European Space Agency’s Sentinel missions (Berger et al., 2012 ) provide a set of quasi-synchronous synthetic aperture radar (SAR) and optical data, systematically acquired worldwide, at high spatial (order of 10m) and temporal (an acquisition up to every five/six days) resolution. This information can be of paramount interest to support large-scale forest dieback assessment and surveillance systems.

While the research community is investigating the benefit related to exploiting multisensor remote sensing information via recent deep learning approaches (Hong et al., 2020 ; Li et al., 2022 ), there is still the necessity to design effective and well-tailored approaches to get the most out of multisensor remote sensing information (Hollaus & Vreugdenhil, 2019 ). This is the case for the large-scale assessment of tree dieback events induced by insect infestations and disease outbreaks where, to the best of our literature survey, existing works (e.g., Andresini et al., 2023 ;Bárta et al., 2021 ;Candotti et al., 2022 ;Dalponte et al., 2022 ;Fernandez-Carrillo et al., 2020 ;Zhang et al., 2022 ) mainly focus on optical data analysis, while no works exist that achieve improvements by leveraging multisensor remote sensing data (e.g., SAR and optical data). In particular, the literature studies to monitor bark beetle infestation in optical data pay high attention to both the data engineering step, through the synthesis of spectral vegetation indices, and the model development step, through the test of various machine learning and deep learning algorithms. On the other hand, similar to research communities where data play a major central role (e.g., computer vision, machine learning, information retrieval), also researchers coming from the remote sensing field are investing efforts towards more systematic and effective exploitation of available data sources. To this end, research actions in this direction have been proposed under the umbrella of data-centric Artificial Intelligence (AI) (Zha et al., 2023 ). Under this movement, the attention of researchers and practitioners is gradually shifting from advancing model design (model-centric AI) to enhancing the quality, quantity and diversity of the data (data-centric AI). Moreover, when remote sensing data are considered, the data-centric AI perspective is even more important since it can steer the community towards developing a methodology to provide further improvements related to the use of highly heterogeneous information to ameliorate the generalization ability with impact on real-world relevant problems and applications (Roscher et al., 2023 ). Nevertheless, the two perspectives (model-centric and data-centric AI) play a complementary role in the larger remote sensing deployment cycle, since standard approaches still struggle to manage and exploit valuable data coming from different and heterogeneous sources as, for instance, in the case of leveraging multisensor complementary information.

With the objective to find a trade-off between data-centric and model-centric achievements in remote sensing and map bark beetle-induced tree dieback events in remote sensing data adopting a semantic segmentation approach (e.g., categorization of pixels into a class), in this paper, we propose DIAMANTE ( D ata-centr I c sem A ntic seg M entation to m A p i N festations in sa T ellite imag E s): a data-centric semantic segmentation approach to train a U-Net like model from a labelled remote sensing dataset prepared using both SAR Sentinel-1 (S1) and multi-spectral optical Sentinel-2 (S2) remote sensing data sources. In particular, for the model development, we compare the achievements of several multisensor data fusion schema that are performed via early, middle or late stages fusion in an underlining U-Net architecture (Ronneberger et al., 2015 ). The U-Net is considered thanks to its wide versatility and increasing popularity, as well as due to the fact that it has been recently used to map bark beetle-induced tree dieback in Sentinel-2 images (Andresini et al., 2023 , 2024 ; Zhang et al., 2022 ). In addition, in this study, we consider that model recycling is one of the achievements to be evaluated in developing a data-centric AI approach. Hence, we start a preliminary investigation of how the multisensor fusion approaches considered in this study may allow us to train a semantic segmentation model for bark beetle detection, which still achieves good performance in a future data setting. The following are the main contributions of this work:

The definition of a remote sensing data collection and curation pipeline to prepare multisensor, Sentinel-1 and Sentinel-2 images of forest areas for which the ground truth map of the bark beetle infestation is available at a specific time. The defined pipeline pays particular attention to the quality of the Sentinel-1 and Sentinel-2 data prepared for the model development.

The adoption and comparison of several multisensor data fusion schemes to combine Sentinel-1 and Sentinel-2 data via early, middle or late stages fusion considering the underlying U-Net architecture.

The extensive assessment of our proposal using a ground truth map of tree dieback induced by bark beetle infestations in the Northeast of France in October 2018. The evaluation examines the performance of models trained and tested using images acquired over non-overlapping scenes in the same period, as well as the temporal forecasting and transferability of the model to an upcoming data setting.

The rest of the manuscript is organized as follows. Related literature is reviewed in Section  2 . The study site and the associated multisensor remote sensing dataset are introduced in Section  3 , while the proposed methodology is described in Section  4 . Section  5 reports the experimental evaluation and it discusses the related findings. Section  6 concludes.

2 Related work

This related work overview is organized into two main fronts. Firstly, we delve into recent remote sensing studies that incorporate machine learning and deep learning to map bark beetle infestation in Sentinel-1 (S1) and Sentinel-2 (S2) images. On the other front, we address the recent achievements of the data-centric artificial intelligence paradigm in remote sensing applications.

2.1 Bark beetle detection in remote sensing

Remote sensing studies to map forest stress related to bark beetle attacks have mainly focused on the analysis of Sentinel-2 data (Estrada et al., 2023 ). These studies are mainly inspired by the analysis conducted in Abdullah et al. ( 2019 ) to explore the effect of several forest disturbances sources (comprising bark beetle infestation) on S2 data. This study shows that the bark beetle infestation, which may affect the biophysical and biochemical properties of trees, is commonly visible via Sentinel-2 multi-spectral imagery. In particular, the chlorophyll degradation and nitrogen deficiency lead to an increase in reflectance spectrum in the visible region (particularly, red and green bands). Changes caused by the reduction of chlorophyll and leaf water have also an effect on Near Infrared (NIR) and Water vapor bands, while diseased and insect attacks affect red-edge bands. This analysis has boosted a plethora of studies (Andresini et al., 2023 , 2024 ; Bárta et al., 2021 ; Candotti et al., 2022 ; Dalponte et al., 2022 ; Fernandez-Carrillo et al., 2020 ; Huo et al., 2021 ; Jamali et al., 2023 ; Zhang et al., 2022 ) that explore the ability of various spectral vegetation indices to enhance the accuracy of decision models trained on Sentinel-2 data. Notice that explored spectral vegetation indices mainly combine red, green, NIR and SWIR (short wave infrared) bands.

Regarding the classification algorithms used to map bark beetle infestations in Sentinel-2 images, the most recent studies have mainly used machine learning algorithms such as Random Forest (Andresini et al., 2023 , 2024 ; Bárta et al., 2021 ; Candotti et al., 2022 ; Huo et al., 2021 ), Support Vector Machine (Andresini et al., 2023 ; Candotti et al., 2022 ; Dalponte et al., 2022 ) and XGBoost (Andresini et al., 2023 , 2024 ). Instead, (Andresini et al., 2023 , 2024 ; Zhang et al., 2022 ) explore the performance of deep learning algorithms under semantic segmentation settings such as U-Net (Andresini et al., 2023 , 2024 ; Zhang et al., 2022 ) and FCN-8 (Andresini et al., 2023 ). To handle the data imbalance situation, (Andresini et al., 2023 , 2024 ; Dalponte et al., 2022 ) use a cost-based learning strategy in combination with Random Forest and Support Vector Machine, while (Andresini et al., 2023 , 2024 ) use the Tversky loss in combination with U-Net and FCN-8. Finally, some studies consider Sentinel-2 time series data to train either Random Forest (Andresini et al., 2024 ; Bárta et al., 2021 ; Fernandez-Carrillo et al., 2020 ) or U-Net models (Andresini et al., 2024 ).

On the other hand, only recently, few remote sensing studies have started exploring the potential of Sentinel-1 data to detect bark beetle infestations. Sentinel-1 data are traditionally used in deforestation detection on Hoekman et al. ( 2020 ). However, (Hollaus & Vreugdenhil, 2019 ) has recently hypothesized that the joint exploitation of Sentinel-1 and Sentinel-2 satellite information can disclose useful information to detect bark beetle infestation hotspots. In particular, this study finds significant differences between Sentinel-1 values measured in infested and healthy sites, respectively. Similar conclusions are drawn in Alshayef and Musthafa ( 2021 ). However, (Alshayef & Musthafa, 2021 ; Hollaus & Vreugdenhil, 2019 ) perform a statistical analysis of Sentinel-1 data distribution without exploring how the use of the Sentinel-1 information can contribute to learning accurate decision models to characterise bark beetle infestations. In general, based on the literature survey, (Hollaus & Vreugdenhil, 2019 ) highlights that significant research effort is still needed to explore the full potential of multisensor data in insect-induced forest disturbance mapping. In this direction, (Huo et al., 2021 ) shows that the joint analysis of Sentinel-1 and Sentinel-2 data marginally contributes to improving the performance of Random Forest models. This conclusion has been recently confirmed also by Konig et al. ( 2023 ) where poor performances have been achieved for bark beetle infestation mapping exploiting only Sentinel-1 radar data and negligible amelioration by the joint exploitation of multisensor (Sentinel-1 and Sentinel-2) data considering both Bayesian and Random Forest classification models. Notably, in Konig et al. ( 2023 ), the multi-sensor data are stacked in a single feature vector that is used as input space for training a classification model. This corresponds to an early fusion schema that concatenates pixel-wise the feature vectors which are acquired with the Sentinel-1 and Sentinel-2 sensors before starting the training stage.

On the other hand, some recent studies have started to investigate how to combine multisensor remote sensing data (e.g., Sentinel-1 and Sentinel-2 data) for the underlying task of land use land cover mapping under a semantic segmentation setting (Sainte Fare Garnot et al., 2022 ). The authors of Li et al. ( 2022 ) have surveyed recent deep learning architectures developed to handle multisensor data comprising Sentinel-1 and Sentinel-2 data. However, this survey mainly considers problems of change detection and biomass estimation without any attention to bark beetle detection problems. In addition, this study points out that the majority of deep neural architectures trained with multisensor satellite data adopt an early fusion mechanism to concatenate pixel-wise data acquired with the Sentinel-1 and Sentinel-2 satellites. The output of the concatenation step is subsequently used as input space for the deep neural model development. In particular, the authors of both Muszynski et al. ( 2022 ) and Solórzano et al. ( 2021 ) learn a U-Net model for land cover classification and flood detection via an early fusion of the Sentinel-1 and Sentinel-2 data. The authors of Altarez et al. ( 2023 ) introduce the Principal Component Analysis (PCA) to combine stacked Sentinel-1 and Sentinel-2 imagery before training a U-Net model for the downstream task of tropical mountain deforestation delineation. On the other hand, a few studies have recently started the investigation of late fusion mechanisms to combine Sentinel-1 and Sentinel-2 data through a deep learning architecture. For example, the authors of Hu et al. ( 2017 ) describe a two-branch architecture that separately extracts features from data acquired with the two distinct satellites and perform the late convolutional fusion before the final decision. A similar late fusion schema is also investigated in Hafner et al. ( 2022 ) for a problem of urban change detection. This study describes an architecture composed of two separate, identical U-Net architectures that process Sentinel-1 and Sentinel-2 image pairs in parallel, and lately fuses extracted features from both sensors at the final decision stage. A middle fusion mechanism is introduced in Audebert et al. ( 2018 ) to perform the fusion of Infrared-Red-Green (IRRG) images and Digital Surface Model (DSM) data extracted from the Lidar point cloud through a SegNet model. Middle fusion is performed at the encoder layers with a simple summation. Imagery data fusion schemes are also discussed in the survey paper (Zhang et al., 2021 ).

In any case, to the best of our knowledge, no previous studies have been proposed yet to explore the opportunity of combining Sentinel-1 and Sentinel-2 data via modern deep learning architecture (i.e., U-Net) for the downstream bark beetle detection task. In addition, this is the first study that frames the investigation of different multisensor fusion schemes (i.e., early fusion, middle fusion and late fusion) in a U-Net development step performed under the umbrella of data-centric AI. On the other hand, neither previous studies have experimented with a fusion mechanism that operates at the encoder level of semantic segmentation models trained on Sentinel-1 and Sentinel-2 data, nor these studies have started the investigation of achievements of data fusion schemes for model development done under the possible lens of model recycling.

2.2 Data-centric artificial intelligence in remote sensing

Data plays a fundamental role in several remote sensing problems, comprising satellite imagery-based forest health monitoring. As a consequence, the emerging data-centric artificial intelligence paradigm (Zha et al., 2023 ) has recently started receiving attention in remote sensing where the big satellite image collections (e.g., the Earth Sentinel-1 and Sentinel-2 image collections acquired via the Copernicus programme) are freely available. Roscher et al. ( 2023 ) describe the main principles of the data-centric artificial intelligence paradigm in geospatial data applications by highlighting that data acquisition and curation should receive as much attention as data engineering and model development and evaluation. This study describes one of the first data-centric remote sensing pipelines experimented for land cover classification in satellite imagery. Phillips et al. ( 2022 ) describe a data-centric approach that uses deep feature extraction to prepare a Sentinel-2 dataset to improve the performance of insect species distribution models. de Carvalho et al. ( 2023 ) describe a data-centric approach that combines semantic segmentation and Geographical Information Systems (GIS) to obtain instance-level predictions of wind plants by using free orbital satellite images. Specifically, this study achieves an improvement of the model performance by including the wind plant shadows to increase the mapped area and facilitate target detection. Ferreira de Carvalho et al. ( 2023 ) investigate the application of iterative sparse annotations for semantic segmentation in remote-sensing imagery, focusing on minimizing the labor-intensive and costly data labeling process. Finally, Schmarje et al. ( 2022 ) describe a data-centric approach for RGB imagery dataset creation that reduces annotation ambiguity for RGB images by combining semi-supervised classification and clustering. To the best of our knowledge, no previous studies have explicitly defined a data-centric semantic segmentation approach that pays specific attention to the data curation step, in addition to the model development step, to support bark beetle infestation mapping considering multisensor remote sensing data provided by Sentinel-1 and Sentinel-2 satellites.

3 Study area and data preparation

This section describes the pipeline realised to prepare the datasets used to train and test the semantic segmentation models. We used Microsoft Planetary Computer Footnote 1 that provides the API to access petabytes of environmental monitoring data comprising Sentinel-1 and Sentinel-2 images from 2016 to the present. Datasets are accessed via Azure Blob Storage. The study site denoted as Northeast France , situated in the northeastern region of France, is predominantly covered by coniferous forests. In 2018 and 2019, a significant proliferation of bark beetles occurred, leading to an estimation by the French National Forestry Office in late April 2019 that approximately 50% of spruce trees in France were infested, contrasting with the typical rate of 15% for dead or diseased trees under normal circumstances. Notably, preceding 2018, there were no instances of substantial windthrows in this area, suggesting that the observed regional-scale attacks were likely spurred by the hot summer droughts experienced in 2018. Satellite data covering the Northeast France study site consists of a Synthetic Aperture Radar (SAR) image acquired via the Sentinel-1 sensor and an optical multi-spectral image acquired via the Sentinel-2 sensor.

3.1 Sentinel-1 and Sentinel-2 data collection

The Sentinel-1 satellite constellation collects polarization data via a C-band synthetic-aperture radar instrument. The C-band denotes a nominal frequency range from 8 to 4 GHz (3.75 to 7.5 cm wavelength) within the microwave (radar) portion of the electromagnetic spectrum. Imaging radars equipped with C-band are generally not hindered by atmospheric effects. They are capable of imaging in all-weather (even through tropical clouds and rain showers), day or night. The constellation is composed of two satellites (Sentinel-1A and Sentinel-1B), and it offers a 6-day exact repeat cycle. This means that, over the same geographical area, one SAR can be accessed every 6 days. Due to the nature of the radar signal, the raw information needs calibration correction related to the terrain topography. For this reason, we adopt the level-1 Radiometrically Terrain Corrected (RTC) product available via the Microsoft Planetary platform Footnote 2 . This product provides SAR images at 10m of spatial resolution. Here we consider the two polarizations VV (Vertical-Vertical) and VH (Vertical-Horizontal). In particular, VV is a mode of radar polarisation where the microwaves of the electric field are oriented in the vertical plane for both signal transmission and reception by means of a radar antenna. VH is a mode of radar polarisation where the microwaves of the electric field are oriented in the vertical plane for signal transmission and where the horizontally polarised electric field of the back-scattered energy is received by the radar antenna. The list of Sentinel-1 bands considered in this study is reported in Table 1 .

The Sentinel-2 satellite constellation retrieves multi-spectral radiometric data (13 bands) in the visible, near infrared, and short wave infrared parts of the spectrum through two satellites (Sentinel-2A and Sentinel-2B). The Sentinel-2 constellation permits covering the majority of the Earth’s surface with a repeat cycle of 5 days. The optical imagery is acquired at high spatial resolution (between 10m and 60 m) over land and coastal water areas. The mission supports a broad range of services and applications such as agricultural monitoring, emergency management or land cover classification. Similarly to the SAR signal, also the optical signal collected by the Sentinel-2 sensors requires corrections. To this end, we adopt the level 2A product available via the Microsoft Planetary platform  Footnote 3 that provides atmospherically corrected surface reflectances. Here we consider all the multi-spectral bands at a spatial resolution of 10m. While bands B2, B3, B4 and B8 are originally at a spatial resolution of 10m, for all the other bands we downscale them at 10m of spatial resolution via the nearest-neighbor resampling based interpolation (Patil, 2018 ). This technique selects the value of the pixel that is nearby the surrounding coordinates of the intended interpolation point. Finally, we ignore the B10 (SWIR - Cirrus) band that is reserved for atmospheric corrections. The final list of Sentinel-2 bands considered in this study is reported in Table 2 . In particular, for each Sentinel-2 band, we report the spatial resolution, the central wavelength, and the band name. The central wavelength refers to the midpoint wavelength at the centre of the spectral band range (barycenter) that the satellite sensor captures. For example, for the B1 band that captures wavelengths from 433 to 453 nanometers (nm), the central wavelength is 443 nm.

3.2 Multisensor data alignment

Let us consider a collection of scenes in Northeast France for which we know the coordinates of each scene geometry and the timestamp in which scenes were observed using both Sentinel-1 and Sentinel-2 sensors. For each scene, we perform two geospatial queries to select a Sentinel-1 and a Sentinel-2 image acquired in a given time interval. The two queries are performed over the Sentinel-1 and Sentinel-2 collections, respectively, using the coordinates of the selected scenes and the selected time interval as query filters. The queried Sentinel-1 and Sentinel-2 images are recorded in the World Geodetic System 1984 ensemble using metric units. As each query may return a resultset of images, we adopt a pipeline to select a representative image from each resultset.

In particular, images are downloaded from Planetary using the STAC API. Footnote 4 For each scene in the study area, we first retrieve the Sentinel-2 image of the scene in a given month by formulating a STAC API query with the parameters “catalogue”, “bbox” and “datetime” set as follows: the value “sentinel-2-l2a” is used as “catalogue”, the “list of the coordinates of the four vertices of the rectangular box of the scene” is used as value for “bbox”, and the “date interval from the first day to the last day of a given month” is used as value for “datetime”. As the Sentinel-2 satellite may record images of the Earth every five days, the resultset of such query may contain several Sentinel-2 images recorded in the sentinel-2-l2a catalogue, covered by the given bbox, and acquired by the satellite within the selected datetime interval. The motivation for querying the sentinel-2-l2a catalogue with a time interval (one month in this study) is that cloud cover, shadows and defective pixels are among the main issues that may affect the Sentinel-2 imagery. The assumption for the success of a model development step performed with Sentinel-2 images is that images have to be as much as possible cloud and defective pixels-free. For this reason, we query Sentinel-2 imagery on a time interval (of one month in this study), to improve the possibility of choosing low-affected Sentinel-2 images in terms of clouds and defective pixels. Hence, we select the Sentinel-2 image of the resultset that achieves the lowest value of “cloud index”. If several images achieve the minimum value of the cloud index in the resultset, then we select the most recent Sentinel-2 image of this selection. The cloud index is computed based on the output of the Scene Classification Level (SCL) algorithm (Louis et al., 2016 ). This information is also recorded as a band in the sentinel-2-l2a catalogue. Specifically, the SCL algorithm uses the reflectance properties of imagery bands to establish the presence or absence of clouds or defective pixels in an image. In this way, it identifies clouds, snow and cloud shadows thus, generating a classification map, which consists of three different cloud classes (including cirrus), together with six additional classes covering shadows, cloud shadows, vegetation, not vegetated, water and snow land covers. For a candidate Sentinel-2 image, the index of cloud is computed as the percentage of imagery pixels that the SCL algorithm recognises as noise, defective, dark, cloud, cloud shadow or thin cirrus.

Given the Sentinel-2 image retrieved for a given scene in the given month, then we formulate the STAC API query to retrieve the Sentinel-1 image that is co-located in space and time with this Sentinel-2 image. The new query is performed by setting the “bbox” parameter as in the query performed to obtain the Sentinel-2 image while setting “catalogue” equal to “sentinel-1-rtc” and “datetime” equal to the “interval from three days before the date of the Sentinel-2 image and three days after the date of the Sentinel-2 image”. The time interval of this query depends on the fact that we would extract a Sentinel-1 image that should be roughly co-located in time with the Sentinel-2 image. On the other hand, Sentinel-1 images are collected every three days with any weather by using a technology not affected by clouds or weather. In addition, we note that noise has been already removed from the Sentinel-1 images that are recorded in the “sentinel-1-rtc” catalogue of Planetary thanks to the application of the Radiometrically Terrain Corrected (RTC) process. This process has been performed before recording the images in the “sentinel-1-rtc” catalogue by using the Ground Range Detected (GRD) Level-1 products produced by the European Space Agency with the RTC processing performed by Catalyst Footnote 5 . Hence, we limit to search the Sentinel-1 images potentially collected before and after the Sentinel-2 image and select the Sentinel-1 image that is the closest in time to the respective Sentinel-2 image.

3.3 Ground truth data, datasets and statistics

We use the ground truth map of the bark beetle infestation hotspots that caused tree dieback in the Northeast of France in October 2018. Footnote 6 This map was commissioned by the French Ministry of Agriculture and Food to Sertit (University of Strasbourg), to assess the damage in spruce forests of the Northeast of France following the 2018 bark beetle outbreak. The remote sensing company WildSense assessed and fixed the infestation hotspot polygons of this map. In particular, to avoid mixed reflectance from various causes in discoloration and defoliation of conifer, WildSense manually selected 87 squared, imagery tiles, covering spruce forestry areas fully under bark beetle attacks in October 2018. The scenes of the final collection cover 1004020 pixels at 10 square meters resolution. The size of the scenes varies from 27 \(\times \) 16 to 296 \(\times \) 319 pixels at 10 square meters resolution, while the percentage of infested territory per scene varies from 0.35% to 34.4% of the scene surface. The total percentage of damaged territory of the entire scene collection is 2.92%. For the experimental evaluation of this research work, we consider 71 scenes (covering 772844 pixels at 10 squared meters resolution) as training scene set and 16 scenes (covering 231176 pixels) as testing scene set. A map of the study scene location and their partitioning in the training set and testing set is depicted in Fig. 1 .

In addition, WildSense identified an extra scene covering spruce forestry areas fully under bark beetle attacks, according to a ground truth map acquired in March 2020. The geographic location of this scene is shown in Fig. 2 . This scene is a tile with size 205 \(\times \) 135 covering 27675 pixels with 10 squared meters as spatial resolution with a percentage of infested territory equal to 3.55%.

Location of the centroids of the study 87 scenes in the Northeast of France area. The red circles correspond to scenes considered for training semantic segmentation models, while the blue circles correspond to scenes considered for evaluating semantic segmentation models

Location of the scene for which the ground truth mask of the bark beetle infestation was acquired in March 2020. The yellow patches map the forest areas with tree dieback caused by the bark beetle

In this study, we prepare four multisensor, satellite datasets populated with both the Sentinel-1 and Sentinel-2 images acquired for each scene in the study area in the Northeast of France. Hence, each dataset is populated with 87 Sentinel-1 images and 87 Sentinel-2 images roughly co-located in time within the same month. Specifically, the four multisensor satellite datasets were obtained by considering Sentinel-1 and Sentinel-2 images acquired monthly for the 87 study scenes in July 2018, August 2018, September 2018 and October 2018, respectively. We partition each imagery dataset into a training set and a testing set by using the same split ratio for each month. In particular, as mentioned above, we select 71 multisensor images as the training set and 16 multisensor images as the testing set for each of these four datasets. Notably, the multisensor images assigned to the four training sets were acquired for the same 71 training scenes although in different months. Similarly, the multisensor images assigned to the four testing sets were acquired for the same 16 testing scenes although in different months.

Box plot distribution of the polarization values measured for the Sentinel-1 bands and the radiometric values measured for the Sentinel-2 bands recorded in the datasets of Sentinel-1 and Sentinel-2 images acquired in the study site in July, August, September and October 2018. Bands are plotted independently with respect to the two opposite classes in the logarithmic scale

The dataset collected in October 2018 – the time at which the ground truth map of the bark beetle-induced tree dieback of the study scenes was produced – is elaborated to analyse the ability to map bark beetle-induced tree dieback in October, while datasets collected for the same scenes from July to September 2018 are elaborated to analyse the ability to predict as earlier as possible signs related to the bark beetle infestation (before trees start dying). Notice that the analysis of satellite imagery data collected in October 2018 follows some communications with foresters reported by Bárta et al. ( 2021 ), according to the beginning of the autumn, i.e., October in this study, may be considered the most suitable period for proactive measures, i.e., for looking for areas of infested trees and removing them from the forest before next spring. On the other hand, the analysis of satellite imagery data collected in July, August and September  2018 is done to explore the performance of the proposed approach in predicting where bark beetle infestation disturbance events are likely to cause future tree dieback. This evaluation is done according to the considerations reported in Kautz et al. ( 2022 ) that the early detection symptoms of bark beetle infestation, which comprise the presence of entrance holes, resin flow from entrance holes and boring dust that occur when the beetles attack the tree, penetrate the bark, and excavate mating chambers and breeding galleries that can be observed through terrestrial fieldwork inventory. So, counting on manually produced labels in the summer months may help the training of semantic segmentation models for automated early detection in scenes uncovered by the forestry fieldwork.

Spearman’s rank correlation coefficient computed between Sentinel-1 and Sentinel-2 bands in the images acquired in the study site in July, August, September and October 2018

Figure 3 shows the box plots of Sentinel-1 and Sentinel-2 data collected in the datasets prepared for this study. All bands are plotted independently of each other for the two opposite ground truth classes (“damaged” and “healthy”). The box plots show that the range of both Sentinel-1 and Sentinel-2 data changes over time. Sentinel-2 data, particularly B5, B6, B7, B8, B8A and B9, show a greater divergence between the opposite classes than Sentinel-1 data, over all the datasets. So, this visual data exploration confirms the general idea that Sentinel-2 contains the most important information to recognize bark beetle infestation hotspots, while Sentinel-1 data can be considered ancillary data that may be used to support analysis of Sentinel-2 data, to gain accuracy in the bark beetle infestation inventory.

In addition, Figure 4 shows the results of the bivariate correlation analysis performed by computing the Spearman’s rank correlation coefficient between Sentinel-1 and Sentinel 2 bands in images acquired between July and October 2018. Spearman’s rank correlation coefficient is a non-parametric measure of rank correlation that assesses how well the relationship between two compared variables can be described using a monotonic function. It varies between -1 and +1 with 0 implying no correlation, -1 implying an exact monotonic relationship with negative correlation and +1 implying an exact monotonic relationship with positive correlation. This correlation analysis shows that the Sentinel-1 bands VV and VH are negatively correlated to the Sentinel-2 bands B1, B2, B3, B4, B5, B11 and B12, while they are positively correlated to Sentinel-2 bands B7, B8, B8A and B9. The Sentinel-2 band B6 passes from showing a low negative correlation with the Sentinel-1 bands VV and VH in July to showing a low positive correlation with the same Sentinel-1 bands in August, September and October. In general, the intensity of the correlation between the Sentinel-2 bands B6, B7, B8, B8A and B9 and the Sentinel-1 bands VV and VH increases from July to August, September and October. In any case, the correlation is close to zero independently of the sign, especially on the bands B6, B7, B8, B8A and B9, which are the Sentinel-2 bands that better separate the opposite classes in the box plot analysis of the same data. Hence, this visual inspection of the collected data confirms a limited correlation between Sentinel-1 and Sentinel-2 data, which is one of the prerequisites for taking advantage of a multisensor approach in model development.

Figure 5 shows the box plot of the cloud index of the Sentinel-2 images selected for this study. This plot shows the high quality of Sentinel-2 images selected in each month. In fact, we are unable to select images with a cloud index lower than 5% only in one image in August 2018 and two images in October 2018. We also note that differences between the box-plot quartiles are slightly higher in October 2018 than in the period July-September 2018. This depends on the expected increase in the frequency of cloudiness as autumn advances.

Box plot of cloud index of Sentinel-2 images acquired in the study site in July, August, September and October 2018

Finally, we collect and prepare the pair of Sentinel-1 and Sentinel-2 images of the scene for which the ground truth map was acquired in March 2020. This pair of images is used in the evaluation stage only, to explore the transferability of the semantic segmentation model learned in October 2018 to subsequent periods. The Sentinel-2 image acquired for this scene in March 2020 and selected in this study has a low noise and cloud index equal to 0.16%. Finally, Figure 6 shows the box plots of both Sentinel-1 and Sentinel-2 data collected in March 2020 for this scene. We note that the outliers of Sentinel-1 data are spread across a lower heat range than that observed in the images collected in the summer and autumn months of 2018. On the other hand, B5, B6, B7, B8, B8A and B9 of Sentinel-2 data still show a remarkable divergence between the opposite classes as in the images collected in the summer and autumn months in 2018.

Box plot distribution of the polarization values measured for the Sentinel-1 bands and the radiometric values measured for the Sentinel-2 bands recorded in the Sentinel-1 image and the Sentinel-2 image acquired in March 2020 for the scene seen in Fig. 2 . Bands are plotted independently to the two opposite classes in the logarithmic scale

4 Semantic segmentation model development

The model development step is performed by leveraging the aligned Sentinel-1 and Sentinel-2 images of scenes for which the ground truth mask of bark beetle infestation is available. Let us consider \(\mathcal {D} = \{ \left( \mathbf {X_{S1}}, \mathbf {X_{S2}}, \textbf{Y}\right) | \mathbf {X_{S1}} \in \mathbb {R}^{H\times W\times 2}, \mathbf {X_{S2}} \in \mathbb {R}^{H\times W\times 12}, \textbf{Y} \in \mathbb {R}^{H\times W\times 1} \}\) a collection of labelled Sentinel-1 and Sentinel-2 images of forest scenes, where every ground truth mask \(\textbf{Y}\) is associated with the images \(\mathbf {X_{S1}}\) and \(\mathbf {X_{S2}}\) , acquired from Sentinel-1 and Sentinel-2 satellites, respectively. For each scene, H and W denote the spatial extent of the monitored scene in terms of scene height and scene width, respectively. The model development step trains a semantic segmentation network from \(\mathcal {D}\) through a U-Net-like architecture that is also in charge of learning the data fusion.

The U-Net architecture is composed of an encoder part and a decoder part. The encoder extracts features. It consists of multiple blocks, where each block is composed of a Batch Normalization layer and a 2D Convolutional layer followed by Max-Pooling for downsampling. At each downsampling step, the height and width of the tensor halves, while the number of channels remains unchanged. The decoder part upsamples the encoded feature maps to the original input shape. It consists of one transposed Convolutional layer for upsampling, followed by multiple blocks, each of which each block consists of a Batch Normalization layer and a 2D Convolutional layer. Skipping connections between the decoder part and the encoder part are used to propagate the spatial information from the earlier layers to the deeper layers to alleviate the vanishing gradients problem (Wu et al., 2019 ). The final classification of each imagery pixel is obtained by using the Sigmoid activation function. The U-Net used in this study is trained via the Tversky loss, which is commonly used to handle imbalanced data (Hinton et al., 2015 ).

Early fusion . Abbreviations: 2D Conv = 2D Convolutional layer; BN=Batch Normalization; S1=Sentinel-1; S2=Sentinel-2

Middle fusion . Abbreviations: 2D Conv = 2D Convolutional layer; BN=Batch Normalization; S1=Sentinel-1; S2=Sentinel-2

Late fusion . Abbreviations: 2D Conv = 2D Convolutional layer; BN=Batch Normalization; S1=Sentinel-1; S2=Sentinel-2

The data fusion mechanism is implemented through three different strategies, namely, Early fusion , Middle fusion and Late fusion , which are defined according to the general classification of multimodal data fusion methods reported in the survey of Zhang et al. ( 2021 ). The Early fusion strategy is the first mechanism adopted in literature for the multimodal data fusion in the deep neural scenario (Couprie et al., 2013 ). It is implemented via a simple concatenation, performed at an early stage, of features from different modalities (i.e., sensors in this study). The concatenation produces a single input space for the model development. In our study, the Early fusion strategy, shown in Fig. 7 , concatenates each pair of images \(\mathbf {X_{S1}}\) and \(\mathbf {X_{S2}}\) obtaining a single hypercube with dimension \({H\times W\times 14}\) . A traditional U-Net architecture is trained on the newly stacked hypercubes.

The Middle fusion strategy combines features learned with the separate branches of a multi-input deep neural network that takes data acquired from different modalities as separate inputs. The fusion is performed at an intermediate layer of the deep neural network. The output of this combination performed at the fusion layer is processed across the subsequent layers of the network until the decision layer. In our study, the Middle fusion strategy, depicted in Fig. 8 uses an architecture with two encoder branches, each taking \(\mathbf {X_{S1}}\) and \(\mathbf {X_{S2}}\) as input, respectively. The output of these branches is fed into a single decoder. The two encoder branches are mapped into a common feature space via a fusion operation and the fusion output is used for the skipping connections. Two fusion operators, named SUM and CONC , are considered in this work for the middle fusion. The SUM operator performs an element-wise summation between the outputs of two parallel blocks in the encoder parts. The CONC operator produces a single hypercube by stacking the outputs of two parallel blocks in the encoder parts. Subsequently, it employs a 2D Convolutional layer to halve the channel size of the output hypercube. This is done to align with the number of channels of the corresponding decoder block for skipping connections. Both the concatenation (Couprie et al., 2013 ; Zhou et al., 2023 ) and the element-wise summation (Park et al., 2017 ; Qian et al., 2023 ) are two common fusion operators used in the literature to fuse multimodal features enclosed in RGB images and Depth images by using CNN-based algorithms. We select these two operators for the Middle fusion strategy performed in this study since they implement two different mechanisms in terms of information retention. In particular, the concatenation operator ( CONC ) allows us to keep all the information from both Sentinel-1 and Sentinel-2 data, where each feature is entirely preserved. On the other hand, the summation operator ( SUM ) provides a more compact representation than the concatenation. In fact, it fuses the features originated from the two sensors into a single vector having the same size of the combined vectors. This operator can be particularly useful when the features are aligned and represent the same spatial locations or attributes.

The Late fusion strategy processes separately input data provided by each modality through distinct deep neural models, and their outputs are combined at the later stage, usually at the classification stage. In our study, the Late fusion strategy, illustrated in Fig. 9 , uses an architecture with two identical, parallel encoder and decoder paths that take as input \(\mathbf {X_{S1}}\) and \(\mathbf {X_{S2}}\) , respectively. The outputs returned by the two decoders are stacked into a single hypercube and the Sigmoid activation function is employed in the final layer. Final considerations concern the expected behaviour of the three data fusion schemes. According to the discussion reported in Zhang et al. ( 2021 ), the Early fusion strategy is expected to better leverage cross-modal information interaction as early as possible in the learning stage. On the other hand, the Late fusion strategy is considered flexible, but it may lack sufficient cross-modal correlation. Finally, the Middle fusion strategy is expected to find a trade-off between Early fusion and Late fusion , with possible advantages in terms of final performances.

5 Empirical evaluation and discussion

5.1 implementation details.

We implemented DIAMANTE in Python 3.0. The source code is available online. Footnote 7 In this study, we consider a U-Net architecture optimized for satellite images implemented using the Keras 2.15 and TensorFlow as back-end Footnote 8 . Both encoder and decoder components of the different variants of U-Net architectures tested in this study are composed of five main blocks. In the encoder part, each block consists of 3 blocks containing a Batch Normalization layer and a 2D Convolutional layer, followed by a \(2 \times 2\) Max-Pooling operation or downsampling. The stride of the Max-Pooling operation was set equal to 2. In the decoder part, each main block consists of a transposed Convolutional layer (for upsampling) followed by 3 blocks containing a Batch Normalization layer and a 2D Convolutional layer. The kernel size of each Convolutional layer was set equal to \(3 \times 3\) . In all hidden layers the Rectified Linear Unit function (ReLU) was used as the activation function, while the Sigmoid activation function was used in the final semantic segmentation layer. The SUM operator was implemented using the Add layer available in TensorFlow. Footnote 9 The training of the U-Net architectures was performed using imagery tiles of size \(32\times 32\) extracted from the imagery scenes by using tiler library. Footnote 10 Both Sentinel-1 and Sentinel-2 data were scaled between 0 and 1 using the Min-Max scaler (as it is implemented in the Scikit-learn 0.22.2 library) In addition, we considered a tile augmentation strategy to improve the performance of the U-Net architecture by using the Albumentations library Footnote 11 . Specifically, we quintupled the number of training imagery tiles by creating new tiles applying traditional computer vision augmentation operators (i.e., Horizontal Flip, Vertical Flip, Random Rotate, Transpose and Grid Distortion). We used the tree-structured Parzen estimator algorithm to optimize hyper-parameters of U-Net architectures (i.e., mini-batch size in { \(2^2\) , \(2^3\) , \(2^4\) , \(2^5\) , \(2^6\) }, learning rate between 0.0001 and 0.01 and image augmentation in {True, False}), by using 20% of the training set as the validation set. In particular, the hyper-parameter configuration that achieves the highest F1 score on the minority class (“damaged”) in the validation set was automatically selected as the best semantic segmentation model. We performed the gradient-based optimisation using the Adam update rule. Finally, each U-Net model was trained with a maximum number of epochs equal to 150, using an early stopping approach to retain the best semantic segmentation model.

5.2 Metrics

To evaluate the accuracy of the semantic segmentation masks, we measured the following metrics: F1 score ( F1 ) computed for the two opposite classes, Macro F1 score ( Macro F1 ) averaged on the two opposite classes and Intersection-over-Union ( IoU ). Specifically, the F1 score measures the harmonic mean of Precision and Recall . The Precision = \(\frac{TP}{TP+FP}\) is the fraction of pixels correctly classified in a specific class ( TP ) among pixels of the considered class ( \(TP+FP\) ). The Recall = \(\frac{TP}{TP+FN}\) is the fraction of pixels correctly classified in a specific class ( TP ) among pixels classified in the considered class ( \(TP+FN\) ). In this study, we computed the F1 score for the two opposite classes of both case studies: “healthy” ( F1(h) ) and “damaged” ( F1(d) ). Macro F1 measures the average of each F1 score value per class, that is, Macro F1 = \(\frac{F1(h) + F1(d)}{2}\) . The IoU score is the ratio of the intersected area to the combined area of prediction and ground truth, that is, IoU = \(\frac{TP}{TP+FP+FN}\) . This is commonly used to evaluate the accuracy of models trained in both semantic segmentation and object detection problems. All metrics are reported in percentages and computed on the images collected for the testing scenes. For each metric, the higher the value, the better the performance of the semantic segmentation masks predicted.

5.3 Results

The illustration of results is organised as follows. Section 5.3.1 presents the results achieved by processing the multisensor imagery dataset collected in the study area in October 2018. This analysis is done to evaluate the performance of the data fusion strategies at the same time the ground truth masks of the study scenes were created. Section 5.3.2 presents a temporal study where we explore the performance of the models trained and evaluated considering satellite images acquired in July, August and September 2018. This analysis is done to explore the ability of the considered data fusion strategies to learn a model capable to perform early detection of tree dieback phenomena. Finally, Section 5.3.3 illustrates the results achieved by considering multisensor semantic segmentation models trained from satellite images acquired in October 2018 to predict the mask of tree dieback caused by a bark beetle infestation in a new scene located in the Northeast of France, but monitored in March 2020. This analysis explores the transferability over time of a semantic segmentation model.

5.3.1 Performance Analysis

In this Section, we analyse the performance of the semantic segmentation masks produced for the testing scenes of the Northeast France study by using the multisensor semantic segmentation models trained via the three data fusion schemes illustrated in Section 4 . As baselines, we consider the single-sensor semantic segmentation models trained with a traditional U-Net by processing either the Sentinel-1 images ( S1 U-Net ) or the Sentinel-2 images ( S2 U-Net ) alone. With regard to the Middle fusion strategy, we report the results achieved with the two fusion operators: SUM and CONC . This evaluation was conducted by processing the dataset of images acquired in October 2018 for both the training and evaluation stages. The accuracy metrics measured on the semantic segmentation masks produced for the images of the testing scenes are reported in Table   3 .

RGB of the Sentinel-2 image acquired in October 2018 for a testing scene of the study area in the Northeast of France ( 10 a). Inventory masks of tree dieback areas caused by bark beetle hotspots in this scene as they are predicted by S1 U-Net ( 10 b), S2 U-Net ( 10 c), Early fusion U-Net ( 10 d), ( 10 g), Middle fusion U-Net with operators SUM ( 10 e) and CONC ( 10 f) and Late fusion U-Net trained on the imagery set acquired in October 2018 for the training scenes of the study area

As we expected, the output of the stand-alone use of Sentinel-1 images is unsatisfactory for this inventory task. In fact, the configuration S1 U-Net achieves the lowest performance in all accuracy metrics. Better performance can be achieved by processing Sentinel-2 images in place of Sentinel-1 images. However, this evaluation study shows that the data fusion of Sentinel-1 and Sentinel-2 images can help us to improve the performance of the semantic segmentation model regardless of the type of data fusion strategy employed. In fact, the Early fusion U-Net , Late fusion U-Net and Middle fusion U-Net all achieve better performance than S2 U-Net that considers Sentinel-2 images only. More in detail, the best configuration in terms of F1(d) , IoU and Macro F1 is achieved with the Middle fusion schemes having Middle fusion (CONC) U-Net as runner-up of Middle fusion (SUM) U-Net . These conclusions are consistent with the observations on the expected behaviour of the data fusion schemes reported in Section 4 . Figure 10 b-g show the semantic segmentation masks of a sample testing scene predicted by the compared models, while Fig. 10 a shows the RGB image of this sample scene. The masks highlight how the use of a data fusion strategy helps us to reduce the number of false alarms in this case. Specifically, the bark beetle infestation masks predicted using the multisensor U-net trained with both Early fusion and Middle fusion schemes show only one false infested patch, while the U-Net trained from Sentinel-1 data shows large extensions of false infested areas and the U-Net trained from Sentinel-2 data shows two false infested patches. Notably, the multisensor U-Net trained with Late fusion strategy removes one of the false patches discovered by S2 U-Net , but, at the same time, it alerts a new false patch that is undetected in the other masks. We note that the Late fusion strategy is the worst-performing fusion strategy of this experiment. This result suggests that although the Late fusion strategy may allow us to correct some false patches detected processing Sentinel-2 data only, it may also produce some artefacts at the decision level, which may cause false alarms unseen in the remaining configurations. Finally, the masks of this example show that the use of SUM operator performs better than the CONC operator in delineating the large damaged patch located on the left side of the scene.

5.3.2 Temporal analysis

To complete this investigation, we illustrate the results of a temporal study conducted to explore the accuracy performance of the semantic segmentation maps produced when the Sentinel-1 and Sentinel-2 images were acquired in the middle of summer (i.e., July 2018) and the late summer (i.e., August 2018 and September 2018), while the ground truth map of the tree dieback was observed in early autumn (October 2018). This analysis is done to explore the performance of the presented data fusion strategies in the early detection of areas where bark beetle infestation disturbance events are likely to cause (near-)future tree dieback. The temporal snapshots of this experiment were selected according to the recent achievements of the analysis on the spectral separability between the healthy and bark beetle attacked trees illustrated in Dalponte et al. ( 2023 ). In particular, this study shows that bark beetle attacks commonly occur in the summer, while the spectral separability between the two opposite classes (“Healthy” and “Damaged”) increases moving from July to October. In addition, it highlights that a time span of approximately one month commonly occurs between the attack of the beetles to a tree and the development of the first symptoms (green-attack) in the tree. Hence, based on the conclusions drawn in this study, the green attack detection stage can reasonably arise in the summer period spanned from July to August. Based on these premises, the accuracy metrics measured on the semantic segmentation maps produced for the testing scenes of this study in each month between July and October 2018 are reported in Table 4 .

These results show that the data fusion of Sentinel-1 and Sentinel-2 continues to help us to gain accuracy also when the multisensor semantic segmentation model is trained to forecast tree dieback areas caused by the bark beetle infestation. Notably, Middle fusion (SUM) U-Net achieves the highest F1(d) , IoU and Macro F1 in segmentation maps produced in experiments performed in July 2018, August 2018 and October 2018. The only exception is observed in the segmentation maps produced for the evaluation in September 2018. However, also in the experiment conducted in September 2018, the Middle fusion (SUM) U-Net still achieves good performance by ranking as the runner-up of the Late fusion U-Net . To draw conclusive conclusions on the better data fusion strategy, we perform the Friedman-Nemenyi test to compare the Macro F1 measured for S1 U-Net , S2 U-Net , Early fusion U-Net , Middle fusion (SUM) U-Net , Middle fusion (CONC) U-Net and Late fusion U-Net on the multiple segmentation maps produced for the testing data of the multisensor datasets of this temporal analysis. This non-parametric test ranks the model configurations compared for each dataset separately, so the best-performing model is given a rank of 1, the second-best rank of 2 and so on. The results of the Friedman-Nemenyi test reported in Fig. 11 shows that the test groups the configurations adopting a multisensor data fusion strategy as statistically different from the configurations that consider either Sentinel-1 data only ( S1 U-Net ) or Sentinel-2 data only ( S2 U-Net ). In addition, the Middle fusion (SUM) U-Net achieves the highest rank by having the Middle fusion (CONC) U-Net as runner-up. Notably, these results of the comparative test support the conclusions already drawn in 5.3.1 and 5.3.3 on the superior performance of a Middle fusion strategy to combine Sentinel-1 and Sentinel-2 data for bark beetle infestation detection.

Comparison of the configurations: Macro F1 measured for S1 U-Net , S2 U-Net , Early fusion U-Net , Middle fusion (SUM) U-Net , Middle fusion (CONC) U-Net and Late fusion U-Net , performed with the Friedman-Nemenyi test run on Macro F1 measured in the temporal analysis performed from July 2018 to October 2018 ( computed \(pvalue=0.013\) )

5.3.3 Transferability analysis

In this Section, we examine the accuracy of the semantic segmentation models learned in October 2018 when used to detect the tree dieback events caused by bark beetle infestations in March 2020. The accuracy metrics measured in this experiment are reported in Table 5 . These results show that also in this evaluation scenario, the data fusion of Sentinel-1 and Sentinel-2 may help us to improve the performance of a semantic segmentation model even when it was trained on past images and used for mapping the bark beetle infestation in future images. The only exception is observed for the Late fusion strategy that achieves lower performance than S2 U-Net . In general, the highest F1(d) , IoU and Macro F1 are achieved with the Middle fusion (CONC) U-Net schema having Middle fusion (SUM) U-Net as runner-up. This confirms the conclusions on the better performance of the Middle fusion strategy already drawn in Section 5.3.1 . Finally, Fig. 12 b-g show the semantic segmentation masks predicted for the scene under evaluation. The RGB image of the scene in March 2020 is shown in Fig. 12 a. The extracts show that the data fusion schemes, except for Late fusion , allow us to reduce the extension of the false alarm areas detected. In both Early fusion and Middle fusion (SUM) schemes, the higher precision is achieved at the cost of a lower recall. Both data fusion configurations allow us to map correctly a percentage of the infested area that is lower than the one mapped processing Sentinel-2 data only. Instead, the use of the Middle fusion (CONC) strategy allows us to achieve the best trade-off between precision and recall in detecting the tree dieback areas caused by the bark beetle infestation. In general, these maps confirm the idea that also when the semantic segmentation model is trained on historical data, the main contribution to the correct detection of the bark beetle infestation is given by Sentinel-2 data, while Sentinel-1 data can aid in reducing false alarms and better delimiting infested areas.

RGB of the Sentinel-2 image acquired in March 2020 ( 12 a). Inventory masks of tree dieback areas caused by bark beetle hotspots in this scene as they are predicted by S1 U-Net ( 12 b), S2 U-Net ( 12 c), Early fusion U-Net ( 12 d), Middle fusion U-Net with operators SUM ( 12 e) and CONC ( 12 f) and Late fusion U-Net ( 12 g) trained on the imagery set acquired in October 2018 for the training scenes of the study area

5.4 Considerations and findings

The experimental assessment highlights the general advantages of using multisensor data over a single data source in various scenarios of bark beetle detection, including early disease detection and out-of-year temporal transfer. While Sentinel-1 alone is not suitable for the considered downstream mapping task, using Sentinel-2 alone yields satisfactory results. However, the combined use of these two publicly available and freely accessible remote sensing data sources provides the best overall results.

More specifically, the joint use of Sentinel-1 and Sentinel-2 data significantly reduces false alarms and improves the delineation of infested areas in the resulting binary maps. Regarding the early detection of bark beetle attacks (Section  5.3.2 ), signs of the attack can be detected with reasonable accuracy one month before the acquisition of ground truth data (September 2018). However, the disease’s early stages (before July 2018) are weakly detectable via satellite imagery.

An additional challenge is represented by the out-of-year transfer of the model trained on 2018 data to 2020 data. Recent studies in the domain of remote sensing analysis have highlighted that spatial and temporal distribution shifts can hinder the direct deployment of a model trained on a particular area or time period to a different area or time period (Capliez et al., 2023 ; Nyborg et al., 2022 ). The results obtained in Section 5.3.3 confirm this point, indicating that there is still room for research activities in the way historical data can be leveraged in order to improve current mapping results. Finally, the comparison of the different approaches indicates that all fusion strategies are statistically significant compared to single source analysis, with the Middle fusion (SUM) U-Net model exhibiting the best average performance. This finding underscores once more the importance of combining multisensor satellite data for mapping tree dieback induced by bark beetle infestation.

6 Conclusion

In this study, we investigate the effectiveness of a data-centric semantic segmentation approach to map forest tree dieback areas caused by bark beetle hotspots. First, we define a data-centric pipeline to collect and prepare images acquired from both the SAR Sentinel-1 sensor and the optical Sentinel-2 sensor. Then, we explore the accuracy performance of several data fusion strategies, namely Early fusion , Middle fusion and Late fusion adopted for the development of a U-Net-like model combining both Sentinel-1 and Sentinel-2 images acquired in the Northeast of France. Finally, we investigate the performance of the proposed strategies in multisensor imagery data acquired in Northeast of France with the map of bark beetle infestation available in October 2018. We conducted the evaluation with imagery data prepared according to the data curation pipeline presented in this study. The experimental results show that multisensor data can actually help us to improve the ability of the U-Net model to detect tree dieback areas caused bark beetle infestations. The evaluation also explores the transferability of the output of the model development step, as well as the performance of the proposed approach in early detection of infestations that will cause tree dieback.

As future work, we plan to continue the investigation of multisensor data fusion strategies in combination with ecological and weather data, as well temporal data trend information. In addition, we plan to extend the investigation of the transferability of the semantic segmentation model, trained with the described multisensor data fusion techniques to unseen data settings. In particular, we intend to start a systematic exploration of some transfer learning approaches to obtain the transferability of a “general” semantic segmentation model trained for a specific disturbance agent to different disturbance agents. For example, we intend to investigate the transferability of a semantic segmentation model trained for mapping forest tree die-back hotspots caused by bark beetle infestation to perform the inventory of tree die-back hotspots caused by different families of fungal forest pathogens. In addition, we hope to be able to acquire large-scale data within the experimental phase of the EU project SWIFTT to be able of investigating, on large scale, the transferability of a semantic segmentation model trained in a geographic area to a different geographic area, in addition to a future time.

Data, Material, and/or Code Availability

The source code is available at https://github.com/gsndr/DIAMANTE

https://planetarycomputer.microsoft.com/

https://planetarycomputer.microsoft.com/dataset/sentinel-1-rtc

https://planetarycomputer.microsoft.com/dataset/sentinel-2-l2a

https://planetarycomputer.microsoft.com/docs/quickstarts/reading-stac/

https://catalyst.earth/

https://macarte.ign.fr/carte/3bd52aa2b6422a3a58b5086576f91080/Foyers+de+scolytes+dans+les+pessi%C3%A8res+et+les+sapini%C3%A8res+du+Nord-Est+de+la+France,+automne+2018-printemps+2019

https://github.com/gsndr/DIAMANTE

https://github.com/karolzak/keras-unet/tree/master

https://www.tensorflow.org/api_docs/python/tf/keras/layers/Add

https://github.com/the-lay/tiler

https://albumentations.ai/

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Acknowledgements

Annalisa Appice acknowledges support from the SWIFTT project, funded by the European Union under Grant Agreement 101082732. Dino Ienco acknowledges support from the Eco2Adapt project, funded by the European Union under Grant Agreement 101059498. Giuseppina Andresini and Vito Recchia are supported by the project FAIR - Future AI Research (PE00000013), Spoke 6 - Symbiotic AI, under the NRRP MUR program funded by the NextGenerationEU. The authors wish to thank the remote sensing company WildSense for preparing the ground truth masks of the evaluation study.

Open access funding provided by Università degli Studi di Bari Aldo Moro within the CRUI-CARE Agreement.

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Giuseppina Andresini, Annalisa Appice & Vito Recchia

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Giuseppina Andresini & Annalisa Appice

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Giuseppina Andresini : Conceptualization, Methodology, Software, Validation, Investigation, Supervision, Writing - original draft, Writing - review & editing Annalisa Appice : Conceptualization, Methodology, Validation, Visualization, Investigation, Writing - original draft, Writing - review & editing, Supervision, Project administration. Dino Ienco : Conceptualization, Methodology, Investigation, Writing - original draft, Writing - review & editing. Vito Recchia : Conceptualization, Methodology, Data curation, Software, Validation, Visualization, Investigation, Writing - review & editing

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Andresini, G., Appice, A., Ienco, D. et al. DIAMANTE: A data-centric semantic segmentation approach to map tree dieback induced by bark beetle infestations via satellite images. J Intell Inf Syst (2024). https://doi.org/10.1007/s10844-024-00877-6

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Received : 27 February 2024

Revised : 24 May 2024

Accepted : 01 August 2024

Published : 04 September 2024

DOI : https://doi.org/10.1007/s10844-024-00877-6

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• Data-centric remote sensing
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The University of Chicago The Law School

Abrams environmental law clinic—significant achievements for 2023-24, protecting our great lakes, rivers, and shorelines.

The Abrams Clinic represents Friends of the Chicago River and the Sierra Club in their efforts to hold Trump Tower in downtown Chicago accountable for withdrawing water illegally from the Chicago River. To cool the building, Trump Tower draws water at high volumes, similar to industrial factories or power plants, but Trump Tower operated for more than a decade without ever conducting the legally required studies to determine the impact of those operations on aquatic life or without installing sufficient equipment to protect aquatic life consistent with federal regulations. After the Clinic sent a notice of intent to sue Trump Tower, the State of Illinois filed its own case in the summer of 2018, and the Clinic moved successfully to intervene in that case. In 2023-24, motions practice and discovery continued. Working with co-counsel at Northwestern University’s Pritzker Law School’s Environmental Advocacy Center, the Clinic moved to amend its complaint to include Trump Tower’s systematic underreporting each month of the volume of water that it intakes from and discharges to the Chicago River. The Clinic and co-counsel addressed Trump Tower’s motion to dismiss some of our clients’ claims, and we filed a motion for summary judgment on our claim that Trump Tower has committed a public nuisance. We also worked closely with our expert, Dr. Peter Henderson, on a supplemental disclosure and on defending an additional deposition of him. In summer 2024, the Clinic is defending its motion for summary judgment and challenging Trump Tower’s own motion for summary judgment. The Clinic is also preparing for trial, which could take place as early as fall 2024.

Since 2016, the Abrams Clinic has worked with the Chicago chapter of the Surfrider Foundation to protect water quality along the Lake Michigan shoreline in northwest Indiana, where its members surf. In April 2017, the U. S. Steel plant in Portage, Indiana, spilled approximately 300 pounds of hexavalent chromium into Lake Michigan. In January 2018, the Abrams Clinic filed a suit on behalf of Surfrider against U. S. Steel, alleging multiple violations of U. S. Steel’s discharge permits; the City of Chicago filed suit shortly after. When the US government and the State of Indiana filed their own, separate case, the Clinic filed extensive comments on the proposed consent decree. In August 2021, the court entered a revised consent decree which included provisions advocated for by Surfrider and the City of Chicago, namely a water sampling project that alerts beachgoers as to Lake Michigan’s water quality conditions, better notifications in case of future spills, and improvements to U. S. Steel’s operations and maintenance plans. In the 2023-24 academic year, the Clinic successfully litigated its claims for attorneys’ fees as a substantially prevailing party. Significantly, the court’s order adopted the “Fitzpatrick matrix,” used by the US Attorney’s Office for the District of Columbia to determine appropriate hourly rates for civil litigants, endorsed Chicago legal market rates as the appropriate rates for complex environmental litigation in Northwest Indiana, and allowed for partially reconstructed time records. The Clinic’s work, which has received significant media attention, helped to spawn other litigation to address pollution by other industrial facilities in Northwest Indiana and other enforcement against U. S. Steel by the State of Indiana.

In Winter Quarter 2024, Clinic students worked closely with Dr. John Ikerd, an agricultural economist and emeritus professor at the University of Missouri, to file an amicus brief in Food & Water Watch v. U.S. Environmental Protection Agency . In that case pending before the Ninth Circuit, Food & Water Watch argues that US EPA is illegally allowing Concentrated Animal Feeding Operations, more commonly known as factory farms, to pollute waterways significantly more than is allowable under the Clean Water Act. In the brief for Dr. Ikerd and co-amici Austin Frerick, Crawford Stewardship Project, Family Farm Defenders, Farm Aid, Missouri Rural Crisis Center, National Family Farm Coalition, National Sustainable Agriculture Coalition, and Western Organization of Resource Councils, we argued that EPA’s refusal to regulate CAFOs effectively is an unwarranted application of “agricultural exceptionalism” to industrial agriculture and that EPA effectively distorts the animal production market by allowing CAFOs to externalize their pollution costs and diminishing the ability of family farms to compete. Attorneys for the litigants will argue the case in September 2024.

Energy and Climate

Energy justice.

The Abrams Clinic supported grassroots organizations advocating for energy justice in low-income communities and Black, Indigenous, and People of Color (BIPOC) communities in Michigan. With the Clinic’s representation, these organizations intervened in cases before the Michigan Public Service Commission (MPSC), which regulates investor-owned utilities. Students conducted discovery, drafted written testimony, cross-examined utility executives, participated in settlement discussions, and filed briefs for these projects. The Clinic’s representation has elevated the concerns of these community organizations and forced both the utilities and regulators to consider issues of equity to an unprecedented degree. This year, on behalf of Soulardarity (Highland Park, MI), We Want Green, Too (Detroit, MI), and Urban Core Collective (Grand Rapids, MI), Clinic students engaged in eight contested cases before the MPSC against DTE Electric, DTE Gas, and Consumers Energy, as well as provided support for our clients’ advocacy in other non-contested MPSC proceedings.

The Clinic started this past fall with wins in three cases. First, the Clinic’s clients settled with DTE Electric in its Integrated Resource Plan case. The settlement included an agreement to close the second dirtiest coal power plant in Michigan three years early, $30 million from DTE’s shareholders to assist low-income customers in paying their bills, and $8 million from DTE’s shareholders toward a community fund that assists low-income customers with installing energy efficiency improvements, renewable energy, and battery technology. Second, in DTE Electric’s 2023 request for a rate hike (a “rate case”), the Commission required DTE Electric to develop a more robust environmental justice analysis and rejected the Company’s second attempt to waive consumer protections through a proposed electric utility prepayment program with a questionable history of success during its pilot run. The final Commission order and the administrative law judge’s proposal for final decision cited the Clinic’s testimony and briefs. Third, in Consumers Electric’s 2023 rate case, the Commission rejected the Company’s request for a higher ratepayer-funded return on its investments and required the Company to create a process that will enable intervenors to obtain accurate GIS data. The Clinic intends to use this data to map the disparate impact of infrastructure investment in low-income and BIPOC communities.

In the winter, the Clinic filed public comments regarding DTE Electric and Consumers Energy’s “distribution grid plans” (DGP) as well as supported interventions in two additional cases: Consumers Energy’s voluntary green pricing (VGP) case and the Clinic’s first case against the gas utility DTE Gas. Beginning with the DGP comments, the Clinic first addressed Consumers’s 2023 Electric Distribution Infrastructure Investment Plan (EDIIP), which detailed current distribution system health and the utility’s approximately $7 billion capital project planning ($2 billion of which went unaccounted for in the EDIIP) over 2023–2028. The Clinic then commented on DTE Electric’s 2023 DGP, which outlined the utility’s opaque project prioritization and planned more than $9 billion in capital investments and associated maintenance over 2024–2028. The comments targeted four areas of deficiencies in both the EDIIP and DGP: (1) inadequate consideration of distributed energy resources (DERs) as providing grid reliability, resiliency, and energy transition benefits; (2) flawed environmental justice analysis, particularly with respect to the collection of performance metrics and the narrow implementation of the Michigan Environmental Justice Screen Tool; (3) inequitable investment patterns across census tracts, with emphasis on DTE Electric’s skewed prioritization for retaining its old circuits rather than upgrading those circuits; and (4) failing to engage with community feedback.

For the VGP case against Consumers, the Clinic supported the filing of both an initial brief and reply brief requesting that the Commission reject the Company’s flawed proposal for a “community solar” program. In a prior case, the Clinic advocated for the development of a community solar program that would provide low-income, BIPOC communities with access to clean energy. As a result of our efforts, the Commission approved a settlement agreement requiring the Company “to evaluate and provide a strawman recommendation on community solar in its Voluntary Green Pricing Program.” However, the Company’s subsequent proposal in its VGP case violated the Commission’s order because it (1) was not consistent with the applicable law, MCL 460.1061; (2) was not a true community solar program; (3) lacked essential details; (4) failed to compensate subscribers sufficiently; (5) included overpriced and inflexible subscriptions; (6) excessively limited capacity; and (7) failed to provide a clear pathway for certain participants to transition into other VGP programs. For these reasons, the Clinic argued that the Commission should reject the Company’s proposal.

In DTE Gas’s current rate case, the Clinic worked with four witnesses to develop testimony that would rebut DTE Gas’s request for a rate hike on its customers. The testimony advocated for a pathway to a just energy transition that avoids dumping the costs of stranded gas assets on the low-income and BIPOC communities that are likely to be the last to electrify. Instead, the testimony proposed that the gas and electric utilities undertake integrated planning that would prioritize electric infrastructure over gas infrastructure investment to ensure that DTE Gas does not over-invest in gas infrastructure that will be rendered obsolete in the coming decades. The Clinic also worked with one expert witness to develop an analysis of DTE Gas’s unaffordable bills and inequitable shutoff, deposit, and collections practices. Lastly, the Clinic offered testimony on behalf of and from community members who would be directly impacted by the Company’s rate hike and lack of affordable and quality service. Clinic students have spent the summer drafting an approximately one-hundred-page brief making these arguments formally. We expect the Commission’s decision this fall.

Finally, both DTE Electric and Consumers Energy have filed additional requests for rate increases after the conclusion of their respective rate cases filed in 2023. On behalf of our Clients, the Clinic has intervened in these cases, and clinic students have already reviewed thousands of pages of documents and started to develop arguments and strategies to protect low-income and BIPOC communities from the utility’s ceaseless efforts to increase the cost of energy.

Corporate Climate Greenwashing

The Abrams Environmental Law Clinic worked with a leading international nonprofit dedicated to using the law to protect the environment to research corporate climate greenwashing, focusing on consumer protection, green financing, and securities liability. Clinic students spent the year examining an innovative state law, drafted a fifty-page guide to the statute and relevant cases, and examined how the law would apply to a variety of potential cases. Students then presented their findings in a case study and oral presentation to members of ClientEarth, including the organization’s North American head and members of its European team. The project helped identify the strengths and weaknesses of potential new strategies for increasing corporate accountability in the fight against climate change.

Land Contamination, Lead, and Hazardous Waste

The Abrams Clinic continues to represent East Chicago, Indiana, residents who live or lived on or adjacent to the USS Lead Superfund site. This year, the Clinic worked closely with the East Chicago/Calumet Coalition Community Advisory Group (CAG) to advance the CAG’s advocacy beyond the Superfund site and the adjacent Dupont RCRA site. Through multiple forms of advocacy, the clinics challenged the poor performance and permit modification and renewal attempts of Tradebe Treatment and Recycling, LLC (Tradebe), a hazardous waste storage and recycling facility in the community. Clinic students sent letters to US EPA and Indiana Department of Environmental Management officials about how IDEM has failed to assess meaningful penalties against Tradebe for repeated violations of the law and how IDEM has allowed Tradebe to continue to threaten public and worker health and safety by not improving its operations. Students also drafted substantial comments for the CAG on the US EPA’s Lead and Copper Rule improvements, the Suppliers’ Park proposed cleanup, and Sims Metal’s proposed air permit revisions. The Clinic has also continued working with the CAG, environmental experts, and regulators since US EPA awarded $200,000 to the CAG for community air monitoring. The Clinic and its clients also joined comments drafted by other environmental organizations about poor operations and loose regulatory oversight of several industrial facilities in the area.

Endangered Species

The Abrams Clinic represented the Center for Biological Diversity (CBD) and the Hoosier Environmental Council (HEC) in litigation regarding the US Fish and Wildlife Service’s (Service) failure to list the Kirtland’s snake as threatened or endangered under the Endangered Species Act. The Kirtland’s snake is a small, secretive, non-venomous snake historically located across the Midwest and the Ohio River Valley. Development and climate change have undermined large portions of the snake’s habitat, and populations are declining. Accordingly, the Clinic sued the Service in the US District Court for the District of Columbia last summer over the Service’s denial of CBD’s request to have the Kirtland’s snake protected. This spring, the Clinic was able to reach a settlement with the Service that requires the Service to reconsider its listing decision for the Kirtland’s snake and to pay attorney fees.

The Clinic also represented CBD in preparation for litigation regarding the Service’s failure to list another species as threatened or endangered. Threats from land development and climate change have devastated this species as well, and the species has already been extirpated from two of the sixteen US states in its range. As such, the Clinic worked this winter and spring to prepare a notice of intent (NOI) to sue the Service. The Team poured over hundreds of FOIA documents and dug into the Service’s supporting documentation to create strong arguments against the Service in the imminent litigation. The Clinic will send the NOI and file a complaint in the next few months.

Students and Faculty

Twenty-four law school students from the classes of 2024 and 2025 participated in the Clinic, performing complex legal research, reviewing documents obtained through discovery, drafting legal research memos and briefs, conferring with clients, conducting cross-examination, participating in settlement conferences, and arguing motions. Students secured nine clerkships, five were heading to private practice after graduation, and two are pursuing public interest work. Sam Heppell joined the Clinic from civil rights private practice, bringing the Clinic to its full complement of three attorneys.