Landsat turns 50: How satellites revolutionized the way we see – and protect – the natural world

Curated on Posted on by Stefaan Verhulst

Article by Stacy Morford: “Fifty years ago, U.S. scientists launched a satellite that dramatically changed how we see the world.

It captured images of Earth’s surface in minute detail, showing how wildfires burned landscapes, how farms erased forests, and many other ways humans were changing the face of the planet.

The first satellite in the Landsat series launched on July 23, 1972. Eight others followed, providing the same views so changes could be tracked over time, but with increasingly powerful instruments. Landsat 8 and Landsat 9 are orbiting the planet today, and NASA and the U.S. Geological Survey are planning a new Landsat mission.

The images and data from these satellites are used to track deforestation and changing landscapes around the world, locate urban heat islands, and understand the impact of new river dams, among many other projects. Often, the results help communities respond to risks that may not be obvious from the ground.

Here are three examples of Landsat in action, from The Conversation’s archive.

Tracking changes in the Amazon

When work began on the Belo Monte Dam project in the Brazilian Amazon in 2015, Indigenous tribes living along the Big Bend of the Xingu River started noticing changes in the river’s flow. The water they relied on for food and transportation was disappearing.

Upstream, a new channel would eventually divert as much as 80% of the water to the hydroelectric dam, bypassing the bend.

The consortium that runs the dam argued that there was no scientific proof that the change in water flow harmed fish.

But there is clear proof of the Belo Monte Dam project’s impact – from above, write Pritam Das, Faisal Hossain, Hörður Helgason and Shahzaib Khan at the University of Washington. Using satellite data from the Landsat program, the team showed how the dam dramatically altered the hydrology of the river…

It’s hot in the city – and even hotter in some neighborhoods

Landsat’s instruments can also measure surface temperatures, allowing scientists to map heat risk street by street within cities as global temperatures rise.

“Cities are generally hotter than surrounding rural areas, but even within cities, some residential neighborhoods get dangerously warmer than others just a few miles away,” writes Daniel P. Johnson, who uses satellites to study the urban heat island effect at Indiana University.

Neighborhoods with more pavement and buildings and fewer trees can be 10 degrees Fahrenheit (5.5 C) or more warmer than leafier neighborhoods, Johnson writes. He found that the hottest neighborhoods tend to be low-income, have majority Black or Hispanic residents and had been subjected to redlining, the discriminatory practice once used to deny loans in racial and ethnic minority communities…(More)”.

What Might Hannah Arendt Make of Big Data?: On Thinking, Natality, and Narrative with Big Data

Curated on Posted on by Stefaan Verhulst

Paper by Daniel Brennan: “…considers the phenomenon of Big Data through the work of Hannah Arendt on technology and on thinking. By exploring the nuance to Arendt’s critique of technology, and its relation to the social and political spheres of human activity, the paper presents a case for considering the richness of Arendt’s thought for approaching moral questions of Big Data. The paper argues that the nuances of Arendt’s writing contribute a sceptical, yet also hopeful lens to the moral potential of Big Data. The scepticism is due to the potential of big data to reduce humans to a calculable, and thus manipulatable entity. Such warnings are rife throughout Arendt’s oeuvre. The hope is found in the unique way that Arendt conceives of thinking, as having a conversation with oneself, unencumbered by ideological, or fixed accounts of how things are, in a manner which challenges preconceived notions of the self and world. If thinking can be aided by Big Data, then there is hope for Big Data to contribute to the project of natality that characterises Arendt’s understanding of social progress. Ultimately, the paper contends that Arendt’s definition of what constitutes thinking is the mediator to make sense of the morally ambivalence surrounding Big Data. By focussing on Arendt’s account of the moral value of thinking, the paper provides an evaluative framework for interrogating uses of Big Data…(More)”.

Expert Group to Eurostat releases its report on the re-use of privately-held data for Official Statistics

Curated on Posted on by Stefaan Verhulst

Blog by Stefaan Verhulst: “…To inform its efforts, Eurostat set up an expert group in 2021 on ‘Facilitating the use of new data sources for official statistics’ to reflect on opportunities offered by the data revolution to enhance the reuse of private sector data for official statistics”.

Data reuse is a particularly important area for exploration, both because of the potential it offers and because it is not sufficiently covered by current policies. Data reuse occurs when data collected for one purpose is shared and reused for another, often with resulting social benefit. Currently, this process is limited by a fragmented or outdated policy and regulatory framework, and often quite legitimate concerns over ethical challenges represented by sharing (e.g., threats to individual privacy).

Nonetheless, despite such hurdles, a wide variety of evidence supports the idea that responsible data reuse can strengthen and supplement official statistics, and potentially lead to lasting and positive social impact.

Having reviewed and deliberated about these issues over several months, the expert group issued its report this week entitled “Empowering society by reusing privately held data for official statistics”. It seeks to develop recommendations and a framework for sustainable data reuse in the production of official statistics. It highlights regulatory gaps, fragmentation of practices, and a lack of clarity regarding businesses’ rights and obligations, and it draws attention to the ways in which current efforts to reuse data have often led to ad-hoc, one-off projects rather than systematic transformation.

The report considers a wide variety of evidence, including historical, policy, and academic research, as well as the theoretical literature… (More)”.

Read the Eurostat report at: https://ec.europa.eu/eurostat/cros/content/read-final-report_en

Unleashing the power of big data to guide precision medicine in China

Curated on Posted on by Stefaan Verhulst

Article by Yvaine Ye in Nature: “Precision medicine in China was given a boost in 2016 when the government included the field in its 13th five-year economic plan. The policy blueprint, which defined the country’s spending priorities until 2020, pledged to “spur innovation and industrial application” in precision medicine alongside other areas such as smart vehicles and new materials.

Precision medicine is part of the Healthy China 2030 plan, also launched in 2016. The idea is to use the approach to tackle some major health-care challenges the country faces, such as rising cancer rates and issues related to an ageing population. Current projections suggest that, by 2040, 28% of China’s population will be over 60 years old.

Following the announcement of the five-year plan, China’s Ministry of Science and Technology (MOST) launched a precision-medicine project as part of its National Key Research and Development Program. MOST has invested about 1.3 billion yuan (US$200.4 million) in more than 100 projects from 2016 to 2018. These range from finding new drug targets for chronic diseases such as diabetes to developing better sequencing technologies and building a dozen large population cohorts comprising hundreds of thousands of people from across China.

China’s population of 1.4 billion people means the country has great potential for using big data to study health issues, says Zhengming Chen, an epidemiologist and chronic-disease researcher at the University of Oxford, UK. “The advantage is especially prominent in the research of rare diseases, where you might not be able to have a data set in smaller countries like the United Kingdom, where only a handful of cases exist,” says Chen, who leads the China Kadoorie Biobank, a chronic-disease initiative that launched in 2004. It recruited more than 510,000 adults from 10 regions across China in its first 4 years, collecting data through questionnaires and by recording physical measurements and storing participants’ blood samples for future study. So far, the team has investigated whether some disease-related lifestyle factors that have been identified in the West apply to the Chinese population. They have just begun to dig into participants’ genetic data, says Chen.

Another big-data precision-medicine project launched in 2021, after Huijun Yuan, a physician who has been researching hereditary hearing loss for more than two decades, founded the Institute of Rare Diseases at West China Hospital in Chengdu, Sichuan province, in 2020. By 2025, the institute plans to set up a database of 100,000 people from China who have rare conditions, including spinal muscular atrophy and albinism. It will contain basic health information and data relating to biological samples, such as blood for gene sequencing. Rare diseases are hard to diagnose, because their incidences are low. But the development of technologies such as genetic testing and artificial intelligence driven by big data is providing a fresh approach to diagnosing these rare conditions, and could pave the way for therapies…(More)”.

Dynamic World

Curated on Posted on by Stefaan Verhulst

About: “The real world is as dynamic as the people and natural processes that shape it. Dynamic World is a near realtime 10m resolution global land use land cover dataset, produced using deep learning, freely available and openly licensed. It is the result of a partnership between Google and the World Resources Institute, to produce a dynamic dataset of the physical material on the surface of the Earth. Dynamic World is intended to be used as a data product for users to add custom rules with which to assign final class values, producing derivative land cover maps.

Key innovations of Dynamic World

  1. Near realtime data. Over 5000 Dynamic World image are produced every day, whereas traditional approaches to building land cover data can take months or years to produce. As a result of leveraging a novel deep learning approach, based on Sentinel-2 Top of Atmosphere, Dynamic World offers global land cover updating every 2-5 days depending on location.
  2. Per-pixel probabilities across 9 land cover classes. A major benefit of an AI-powered approach is the model looks at an incoming Sentinel-2 satellite image and, for every pixel in the image, estimates the degree of tree cover, how built up a particular area is, or snow coverage if there’s been a recent snowstorm, for example.
  3. Ten meter resolution. As a result of the European Commission’s Copernicus Programme making European Space Agency Sentinel data freely and openly available, products like Dynamic World are able to offer 10m resolution land cover data. This is important because quantifying data in higher resolution produces more accurate results for what’s really on the surface of the Earth…(More)”.

Impediment of Infodemic on Disaster Policy Efficacy: Insights from Location Big Data

Curated on Posted on by Stefaan Verhulst

Paper by Xiaobin Shen, Natasha Zhang Foutz, and Beibei Li: “Infodemics impede the efficacy of business and public policies, particularly in disastrous times when high-quality information is in the greatest demand. This research proposes a multi-faceted conceptual framework to characterize an infodemic and then empirically assesses its impact on the core mitigation policy of a latest prominent disaster, the COVID-19 pandemic. Analyzing a half million records of COVID-related news media and social media, as well as .2 billion records of location data, via a multitude of methodologies, including text mining and spatio-temporal analytics, we uncover a number of interesting findings. First, the volume of the COVID information incurs an inverted-U-shaped impact on individuals’ compliance with the lockdown policy. That is, a smaller volume encourages the policy compliance, whereas an overwhelming volume discourages compliance, revealing negative ramifications of excessive information about a disaster. Second, novel information boosts policy compliance, signifying the value of offering original and distinctive, instead of redundant, information to the public during a disaster. Third, misinformation exhibits a U-shaped influence unexplored by the literature, deterring policy compliance until a larger amount surfaces, diminishing informational value, escalating public uncertainty. Overall, these findings demonstrate the power of information technology, such as media analytics and location sensing, in disaster management. They also illuminate the significance of strategic information management during disasters and the imperative need for cohesive efforts across governments, media, technology platforms, and the general public to curb future infodemics…(More)”.

Decoding human behavior with big data? Critical, constructive input from the decision sciences

Curated on Posted on by Stefaan Verhulst

Paper by Konstantinos V. Katsikopoulos and Marc C. Canellas: “Big data analytics employs algorithms to uncover people’s preferences and values, and support their decision making. A central assumption of big data analytics is that it can explain and predict human behavior. We investigate this assumption, aiming to enhance the knowledge basis for developing algorithmic standards in big data analytics. First, we argue that big data analytics is by design atheoretical and does not provide process-based explanations of human behavior; thus, it is unfit to support deliberation that is transparent and explainable. Second, we review evidence from interdisciplinary decision science, showing that the accuracy of complex algorithms used in big data analytics for predicting human behavior is not consistently higher than that of simple rules of thumb. Rather, it is lower in situations such as predicting election outcomes, criminal profiling, and granting bail. Big data algorithms can be considered as candidate models for explaining, predicting, and supporting human decision making when they match, in transparency and accuracy, simple, process-based, domain-grounded theories of human behavior. Big data analytics can be inspired by behavioral and cognitive theory….(More)”.

Police surveillance and facial recognition: Why data privacy is an imperative for communities of color

Curated on Posted on by Stefaan Verhulst

Paper by Nicol Turner Lee and Caitlin Chin: “Governments and private companies have a long history of collecting data from civilians, often justifying the resulting loss of privacy in the name of national security, economic stability, or other societal benefits. But it is important to note that these trade-offs do not affect all individuals equally. In fact, surveillance and data collection have disproportionately affected communities of color under both past and current circumstances and political regimes.

From the historical surveillance of civil rights leaders by the Federal Bureau of Investigation (FBI) to the current misuse of facial recognition technologies, surveillance patterns often reflect existing societal biases and build upon harmful and virtuous cycles. Facial recognition and other surveillance technologies also enable more precise discrimination, especially as law enforcement agencies continue to make misinformed, predictive decisions around arrest and detainment that disproportionately impact marginalized populations.

In this paper, we present the case for stronger federal privacy protections with proscriptive guardrails for the public and private sectors to mitigate the high risks that are associated with the development and procurement of surveillance technologies. We also discuss the role of federal agencies in addressing the purposes and uses of facial recognition and other monitoring tools under their jurisdiction, as well as increased training for state and local law enforcement agencies to prevent the unfair or inaccurate profiling of people of color. We conclude the paper with a series of proposals that lean either toward clear restrictions on the use of surveillance technologies in certain contexts, or greater accountability and oversight mechanisms, including audits, policy interventions, and more inclusive technical designs….(More)”

Researcher Helps Create Big Data ‘Early Alarm’ for Ukraine Abuses

Curated on Posted on by Stefaan Verhulst

Article by Chris Carroll: From searing images of civilians targeted by shelling to detailed accounts of sick children and their families fleeing nearby fighting to seek medical care, journalists have created a kaleidoscopic view of the suffering that has engulfed Ukraine since Russia invaded—but the news media can’t be everywhere.

Social media practically can be, however, and a University of Maryland researcher is part of a U.S.-Ukrainian multi-institutional team that’s harvesting data from Twitter and analyzing it with machine-learning algorithms. The result is a real-time system that provides a running account of what people in Ukraine are facing, constructed from their own accounts.

The project, Data for Ukraine, has been running for about three weeks, and has shown itself able to surface important events a few hours ahead of Western or even Ukrainian media sources. It focuses on four areas: humanitarian needs, displaced people, civilian resistance and human rights violations. In addition to simply showing spikes of credible tweets about certain subjects the team is tracking, the system also geolocates tweets—essentially mapping where events take place.

“It’s an early alarm system for human rights abuses,” said Ernesto Calvo, professor of government and politics and director of UMD’s Inter-Disciplinary Lab for Computational Social Science. “For it to work, we need to know two basic things: what is happening or being reported, and who is reporting those things.”

Calvo and his lab focus on the second of those two requirements, and constructed a “community detection” system to identify key nodes of Twitter users from which to use data. Other team members with expertise in Ukrainian society and politics spotted him a list of about 400 verified users who actively tweet on relevant topics. Then Calvo, who honed his approach analyzing social media from political and environmental crises in Latin America, and his team expanded and deepened the collection, drawing on connections and followers of the initial list so that millions of tweets per day now feed the system.

Nearly half of the captured tweets are in Ukrainian, 30% are in English and 20% are in Russian. Knowing who to exclude—accounts started the day before the invasion, for instance, or with few long-term connections—is key, Calvo said…(More)”.

The ethical imperative to identify and address data and intelligence asymmetries

Curated on Posted on by Stefaan Verhulst

Article by Stefaan Verhulst in AI & Society: “The insight that knowledge, resulting from having access to (privileged) information or data, is power is more relevant today than ever before. The data age has redefined the very notion of knowledge and information (as well as power), leading to a greater reliance on dispersed and decentralized datasets as well as to new forms of innovation and learning, such as artificial intelligence (AI) and machine learning (ML). As Thomas Piketty (among others) has shown, we live in an increasingly stratified world, and our society’s socio-economic asymmetries are often grafted onto data and information asymmetries. As we have documented elsewhere, data access is fundamentally linked to economic opportunity, improved governance, better science and citizen empowerment. The need to address data and information asymmetries—and their resulting inequalities of political and economic power—is therefore emerging as among the most urgent ethical challenges of our era, yet often not recognized as such.

Even as awareness grows of this imperative, society and policymakers lag in their understanding of the underlying issue. Just what are data asymmetries? How do they emerge, and what form do they take? And how do data asymmetries accelerate information and other asymmetries? What forces and power structures perpetuate or deepen these asymmetries, and vice versa? I argue that it is a mistake to treat this problem as homogenous. In what follows, I suggest the beginning of a taxonomy of asymmetries. Although closely related, each one emerges from a different set of contingencies, and each is likely to require different policy remedies. The focus of this short essay is to start outlining these different types of asymmetries. Further research could deepen and expand the proposed taxonomy as well help define solutions that are contextually appropriate and fit for purpose….(More)”.