This issue brief was produced by FP Analytics, the independent research division of Foreign Policy magazine, and underwritten by Maxar.

In 1906, George R. Lawrence famously used a system of kites and a 20 kg camera to capture photos of earthquake-stricken San Francisco, in what many see as the first systematic use of aerial imagery to map impacts on the ground.1 More than a century later, the essential goals of geospatial imaging are the same—to capture information about conditions on the ground and, in the case of crisis management, help inform decisionmaking and save lives.

This task has evolved along with the proliferation and increasing sophistication of aerial and space-based assets. Outside of military and commercial uses, modern geospatial imaging and sensory data have seemingly endless uses in the realms of disaster response and crisis management. Such data can play a role in preventing, mitigating, and recovering from natural disasters; help track global climate change at both the granular and macroscopic levels; and give critical insights in crises with more immediate human causes, such as conflicts and political unrest.

Saving Lives Before, During, and After Natural Disasters

While not all natural disasters are detectable before they occur, geospatial imaging from the air and space can capture early signs of events such as storms, landslides, and droughts and help identify vulnerable localities and populations. The most familiar uses are likely those associated with storms, including hurricanes and cyclones. Platforms such as NOAA’s Geostationary Operational Environmental Satellites (GOES) can help accurately predict landfall of major storms, potentially saving thousands of lives and giving precious time to governments and other response organizations to evacuate vulnerable populations in harm’s way.2 For example, two nearly identical cyclones in India 15 years apart led to drastically different death tolls, thanks in large part to accurate forecasting. In 2014, Cyclone Hudhud killed 46 people; a comparable storm in 1999 killed more than 10,000.3

However, the role of geospatial data in disaster prevention stretches far beyond conventional weather forecasting. Knowledge of the density and state of vegetation can highlight areas where deforestation and desertification make conditions ripe for wildfires, or capture early signs of particularly dry years and the threat of drought.4 When overlaid with satellite-derived population data, especially in fragile states with limited on-the-ground information, vulnerable populations can be identified, and preventative action, taken before conditions reach a crisis—minimizing risk to human life and overall cost of response. Satellite imagery can also produce fine-resolution elevation models that, when mixed with knowledge of deforestation, can highlight areas highly susceptible to landslides—the most frequent type of natural disaster—giving decisionmakers and emergency response personnel an opportunity to respond before the ground starts to shift.5

Even in the best of scenarios, forewarning can only help so much, especially considering the coverage needed to monitor populations around the globe in the context of numerous potential threats. That is why the ability to rapidly utilize satellites and aerial assets to help in an ongoing crisis, and quickly analyze and disseminate relevant data, is essential. Especially in remote or unmapped areas and dangerous terrain, geospatial imaging offers a means of relaying critical information to relevant parties to identify the worst-impacted communities, direct first-responders mid-crisis, establish where key infrastructure has been damaged or destroyed, and identify populations still in harm’s way.6

Critical Context for National Security and Disaster Response
Geospatial imaging during and after a crisis is critical to supporting homeland security and disaster relief efforts. Eagle Vision, a program by the U.S. Air Force and Air National Guard, uses rapidly deployable downlink ground stations to secure critical imagery in crisis scenarios. These mobile stations give responders an advantage in remote locations with limited infrastructure, since quick access to up-to-date imaging can save lives. In Puerto Rico in 2017, Eagle Vision provided fresh imagery for route planning, helicopter landing zone analysis, and hazard identification in a landscape drastically altered by Hurricane Maria.7

Some disasters happen in an instant, but others unfold in stages, making flexibility and sophistication in geospatial imaging technology key factors in effective response. Earthquakes, for example, can occur with little warning and leave devastating, varied impacts. Satellites and aerial assets with sufficient imaging resolution can be used in the immediate aftermath to rapidly identify impacted blocks or even, under the right conditions, damage to specific buildings.8 The classification of affected structures, once solely the brunt of human analysts, is now increasingly made easier through the use of machine learning tools that, once trained, can interpret imagery with comparable accuracy to human experts—in hours rather than days.9 In the case of a rapidly moving, ongoing disaster such as a wildfire, geospatial imagery can be used to track the spread of new fires, to identify vulnerable people and structures in the woodland-urban interface, or to find safe routes for firefighters to fight or retreat.10 When dangerous conditions peak over a large area, as in the case of floods, radar imaging, which is unaffected by the cloud coverage that commonly accompanies flooding, can delineate the extent of floodwaters and help predict their recession, tasks that can be dangerous or even impossible from the ground.11

Detecting Damage to Direct Emergency Response

Slide to see damage from Hurricane Dorian

Algorithms run on satellite imagery before and after a hurricane help first responders identify damaged structures and take action quickly. Satellite photos show Marsh Harbor in the Bahamas before and after Hurricane Dorian hit the area in September 2019. MAXAR TECHNOLOGIES

Long after floodwaters recede, or the ground stops shaking, insights from above can facilitate the most efficient use of resources in recovery efforts and provide critical information about affected populations over a longer period. In the worst-case scenarios, populations resettle temporarily or need to move permanently. Satellites can show where populations may have taken shelter, provide estimates of the number of people affected, and allow for an inventory of resources and infrastructure that can elucidate obstacles to a longer-term recovery plan.12 For example, in places where wood-burning is critical for heat and cooking, cross-referencing the number of affected people in an area with the rate of loss of vegetation can indicate how long a particular area is suitable for temporary settlements and can help identify more suitable locations when needed.13 Progress in rebuilding, especially in the context of key infrastructure projects such as roads and water systems, can be monitored from afar and cross-referenced with on-the-ground interviews to gauge progress in dangerous and remote areas.14

Additional context given by accurate mapping of people and the built environment can help governments and non-governmental organizations zero in on sustainable, long-term recovery plans. As climate change may increase the frequency and severity of natural disasters, information garnered through geospatial imaging will inform hard choices, such as whether to rebuild or resettle, leveraging satellites to improve the lives of generations to come.15

Fighting Climate Change From Above

The scope of climate change, from sea-level rise to desertification, requires knowledge at both the granular and macro levels. Advances in geospatial imaging technology are allowing us to see individual data points with clarity, such as dry riverbeds or endangered megafauna, but also the big picture, from global emissions to region-wide shifts in the landscape.16 Satellites and aerial assets can provide the critical insights that decisionmakers and NGOs need to monitor, respond to, and prepare for human development challenges amid a changing climate. 

Key to gauging the scope of climate change is accurate and up-to-date knowledge of basic metrics such as temperature and emissions. Since the 1970s, with the launch of Landsat-1, the United States and others have been collecting climate data from space, but this technology continues to improve and can provide insights that are inaccessible to monitors on the ground. With temperature, that includes worldwide coverage of temperature measurements from remote and inaccessible areas—information that is vital, in combination with ground measurements, for accurately assessing the pace of global warming.17 Pollution-monitoring satellites such as the European Space Agency’s Copernicus Sentinel-5P and Japan’s GOSAT and GOSAT-2 allow scientists to measure gases such as carbon dioxide and methane from space.18 New efforts, such as Climate TRACE, may even permit the near-real-time tracking of pollution, which would help capture adherence to, and contravention of, Nationally Determined Contribution plans to the Paris Agreement.19

Collecting Real-time Climate Data
Geospatial platforms are offering promising new methods for collecting essential data that is relevant to climate change. In 2022, TEMPO, a collaborative effort between NASA and the Smithsonian Astrophysical Observatory, will place instruments aboard a Maxar geostationary satellite above North America to provide hourly monitoring of air pollution.20 In a similar vein, Climate TRACE, an effort by Google and a number of non-profit organizations, seeks to launch a platform to track greenhouse gas emissions worldwide using pre-existing satellite infrastructure as well as aerial-based assets. By leveraging tools such as ultraviolet spectrometers, nitrogen oxide sensors, and machine learning to process data, these programs hope to demonstrate how satellites can create bold clarity about pollution and emissions.21 This is particularly useful because accurately tracking such data locally and around the world is technically challenging and often involves estimates and self-reporting, which authorities do not always have the capacity to verify.22 Real-time data could help measure population exposure, improve air-quality forecasting, and identify illegal polluters and companies or countries breaking their climate commitments.23

Perhaps the greatest advantage of geospatial imaging is the ability to look at the planet through both microscopic and macroscopic lenses.24 Close to the ground, projects such as NASA’s IceBridge use planes to monitor Antarctic glacial melt from heights of as little as 100 feet, harnessing a combination of tools to collect data both above and below the ice.25 Meanwhile, satellites such as those in NASA’s GOES system maintain geostationary orbit, generating a continuous record of conditions on the surface from 22,000 miles away.26 Tools across space-based and aerial platforms can produce varied data on climate change both locally and globally. Altimeter measurements can gauge rising sea levels or measure snowpack at wintertime, generating knowledge of what may lie ahead next season or next century. Near-infrared imaging focused on vegetation can track deforestation in the Amazon or desertification in the Sahel. And knowledge of shifting, landscape-wide conditions carries geopolitical weight as well, from the identification of climate-driven insecurity, such as a drought that leads to forced migration, to the opening of new realms for great power competition. In the Arctic, satellites have helped document the opening-up of new sea routes due to melting sea ice, as well as an accompanying revitalization of Soviet-era bases as Russia moves to capitalize on a new theater of competition.27

Monitoring Vegetation Loss and Hotspots During Wildfires

Slide to see use of infrared imagery in California.

Short-wave infrared imagery (left) taken by Maxar’s WorldView-3 satellite penetrates smoke to show burned vegetation and hotspots on the ground during the River Fire in Toro Park, California in August 2020. The image at right shows the burned area in natural color a few days later. MAXAR TECHNOLOGIES

Understanding climate change goes further than acknowledging the ways the natural environment is changing—it requires clarity on how human behavior is impacting and interacting with the planet. That means that accurate knowledge of human development, particularly in and around the areas most impacted by a changing climate, is critical to mitigating its effects and identifying where humans are exacerbating climate crises. Satellites and aerial assets are already well suited for mapping buildings, and when paired with on-the-ground knowledge, they can produce pictures of population density and movements.28 That can shape our understanding of growing cities and the resources they will demand, how urbanization may threaten essential ecosystems and natural landscapes, and how climatic shifts can lead to national security threats. The more of this data we collect, the better we will understand the relationships between humans and the planet. 

Conflict Response and Other Humanitarian Crises

The advantages of geospatial imagery and other sensor data also apply to humanitarian crises and security contexts with more immediate human causes, such as conflict, migration, and unrest. Above all, an eye above provides a level of insight into conditions on the ground over vast areas, including in places that have weak governance or mountainous terrain and might be dangerous to government and NGO personnel. By helping to identify patterns and vulnerability hotspots, geospatial imaging can enable more targeted responses to affected populations, rapid identification of the outbreak of violence, and, in some cases, a record of events. 

Some of these uses are helpful for tracking specific incidents and informing policy responses, especially in the realms of security and crisis management. For example, MODIS (Moderate Resolution Imaging Spectroradiometer, a type of imaging from NASA) has been used in places such as Sudan, Kenya, and Myanmar to look for fires related to the outbreak of violence, showing potential to serve as an early-warning system over large areas.29 Other signs of violence, such as physical damage to the landscape or shifting patterns in nighttime light usage, can also be picked up and may provide intelligence about the state of a conflict to humanitarian actors and defense agencies alike.30 Depending on the availability of assets and their imaging capacity, they can be leveraged to look for and track groups of people or to identify military equipment, providing an indication of conditions and intentions on the ground.31 For example, satellites have been used extensively to document the spread of Russian private military contractors across Africa and elsewhere, helping to expose Russian gray zone activity.32

A Bird’s-Eye View of an Epidemic
Even in the case of disease outbreaks, geospatial imaging can be integral to right-sizing and targeting response efforts. During an Ebola outbreak in Guinea in 2014, an initiative by Humanitarian OpenStreetMap Team and Doctors Without Borders combined satellite imagery with limited on-the-ground resources to rapidly map three cities and estimate their current population sizes and densities. This imagery, enabled by a partnership with Maxar, provided critical context to epidemiological surveys that were seeking to understand the scope of the Ebola outbreak in these cities, which helped tailor effective epidemic response.33

Other uses appear over the long-term in the aftermath of a conflict, such as gauging how a conflict may have affected populations, food production, infrastructure, and trade. In Mali following a rise in insecurity in 2019, the World Food Programme (WFP) leveraged a combination of satellite-derived data to map 3,200 localities in the region of Mopti, gauge their agricultural productivity, and determine what happened to populations following the onset of intercommunal violence.34 The WFP was able to document a direct relationship between violent incidents and reduced agricultural capacity, information that could then be used to shape response efforts to reflect new conditions on the ground, including understanding that the effects were felt acutely by specific localities but almost not at all by others. In this sense, identifying places that are impacted by a crisis can be as valuable as identifying places that are not, since misallocated aid in a resource-scarce environment can mean the inability to help others in need.35

Geospatial data can also shed light in instances where malicious actors may be trying to obscure their behavior, and serve as a record of people, places, assets, and confrontations. For example, geospatial intelligence (GEOINT) can be used by security apparatus and other actors to identify war crimes and atrocities, as when information from a survivor in Iraq was used to help authorities locate a mass grave.36 Likewise, an eye above can serve as a check on the accountability of authoritarian regimes such as China and Russia, which often seek to maintain deniability for actions that the global community would consider problematic. In Xinjiang, satellite imagery has been repeatedly used to document the extent and expansion of detention facilities that are part of the Chinese Communist Party’s campaign against China’s embattled Uyghur minority.37 Similarly, satellite imagery during Russia’s annexation of Crimea and the subsequent fighting in Ukraine was used to illustrate clearly on the international stage how closely Russian forces were supporting forces in Crimea, even if they continued to deny doing so.38

As with monitoring natural disasters and climate change, the value of satellites in the realm of humanitarian response and crisis management will increase as technology improves, methods of use are refined, and information is better shared among private, public, and international partners.

Technological Opportunities and Obstacles

Geospatial data can facilitate a range of humanitarian and crisis responses, but its applications should not be overstated. In nearly all the aforementioned use cases, the availability and accessibility of data can be an obstacle. New technologies, from improved imaging techniques to machine learning, can help alleviate these issues and make the most of the vast quantities of data, but conscientious, human-centered designs remain essential.

The state of imaging and sensory technology is a key determinant of what satellites and aerial platforms can feasibly accomplish. These systems collect a wide variety of data, from lower-resolution imagery, where each pixel represents 60 meters or more, to very-high-resolution (VHR) imagery, where each pixel can represent as little as 30 centimeters, as well as a host of non-visual sensing data.39 Each type is particularly suited for specific tasks, with low-resolution imagery best for landscape-wide changes, VHR imagery more suitable for intelligence collection, and non-imaging tools suited for other uses, such as capturing topography.40 This makes the availability of platform types a key factor in the matrix that determines their value to government agencies, NGOs, and others. 

In the case of satellites, simple differences in revisit time—the time elapsed between a satellite’s observations of the same point on earth—or in the angle of imaging can greatly complicate analysis, as changes in shadows, lighting, weather, or other factors can strongly impact interpretation of events on the ground.41 New technology may deliver some answers. CubeSats—constellations of smaller but more numerous satellites—can help ameliorate issues with revisit time by creating more complete and continuous coverage, but at the sacrifice of carrying less sophisticated imaging equipment that can fit on a smaller platform.42 Many traditional earth-imaging satellites orbit in a sun-synchronous pattern, meaning they image an area at the same local time each day. Maxar’s WorldView Legion satellites, which are set to launch later this year, will also feature mid-inclination orbits. That will allow Maxar’s constellation to capture certain areas of the world 15 times per day, which is crucial frequency for responding to rapidly changing situations.43 Aerial platforms, such as unmanned aerial vehicles (UAVs), have their own advantages, including rapid deployment and lower costs, but also their own limitations, such as limited flight time and data storage.44

Perhaps the biggest technological driver in facilitating the use of geospatial imaging and sensor data is the growing role of machine learning. Such tools can help human operators isolate useful elements from data more rapidly and assist in imagery analysis, such as locating populations in remote areas or identifying the number of houses or tents in a refugee camp.45 Such systems include deep learning autoencoders (which look at images and assign each pixel a new value to permit the rapid classification of land, water, urban areas, and vegetation) and object detection (automated identification of where distinct “things” begin and end, such as specific buildings or fire damage).46

Machine learning techniques are an industry imperative, given the demands of Big Data. For example, as of 2018, Maxar’s earth-imaging WorldView constellation of satellites was accumulating more than 80 terabytes of data per day—the equivalent of roughly 20 million standard photos from an iPhone 12.47 The sheer quantity of data makes machine learning increasingly valuable for identifying usable information, and some models even demonstrate predictive capacity to see patterns and insights that human operators may miss. Typically, however, the effectiveness of such systems depends on conscientious designs, with human involvement, that can leverage the advantages of both machine learning systems and human assessment.

Building Collaborative Solutions for Geospatial Data

Partnerships among governments, private-sector companies, and NGOs offer a promising route to tackle some of the obstacles posed by geospatial data. Some of the greatest barriers for potential users include the costs of acquiring and processing geospatial imagery and the limited availability of high-quality, up-to-date imagery for public use. Likewise, the tools and technical expertise needed to process and handle such data can be beyond the capacity of some government entities, particularly in developing countries, as well as NGOs whose mission is to efficiently deliver relief on the ground. Acknowledging these bottlenecks, some companies such as Maxar and Planet have made high-resolution data freely available to select researchers.48 And to ameliorate the barriers associated with Big Data, tech companies such as Google, Amazon, and Facebook are making agreements to allow external partners, including NGOs and university researchers, to harness their cloud computing capacity and other technical infrastructure to handle the immense computing needs that accompany geospatial data.49

Satellite imagery fuels the creation of accurate 3D models, like this one of Aleppo, Syria. 3D data provides additional context for governments and response organizations. MAXAR TECHNOLOGIES

Collaborative efforts are also proliferating at the state and intergovernmental levels, with programs and initiatives such as UNOSAT, Sentinel Asia, the Copernicus Emergency Management Service, and the International Charter “Space and Major Disasters” aiding the cooperative use of imagery and other data to respond to crisis scenarios around the world. Sentinel Asia, for example, includes 111 government and non-government partners in the Asia-Pacific in a voluntary effort to distribute disaster information in near-real time.50 This type of international data sharing can save lives and aid recovery efforts, as demonstrated by Cyclone Idai in Mozambique in 2019. After landfall, public and private entities in Brazil and the United Kingdom and international bodies such as UNOSAT teamed up to map the extent of impacts and inform a more targeted government response.51

The collaborative networks forming among governments and public and private entities are key forums to address some of the challenging questions about the moral and legal obligations associated with the use of geospatial imagery and other data, notably issues of privacy.52 Furthermore, decisions about how and when to use geospatial data can be deceivingly complex. Harvard researchers encountered this issue in 2016 when a group of White Helmets, also known as Syria Civil Defence, desperately sought assistance in finding an evacuation route from Aleppo, Syria. Moral uncertainties and knowledge limitations ended up pre-empting action, rendering intervention simply untenable, despite the parties having had the capacity to do so.53 Even services that seem straightforwardly beneficial, such as mapping remote communities, can have pernicious implications, for example, if that data is then used by a regime to exert control over a community. As with maximizing the functional benefit of the technology of satellites, collaborative, international engagements with private and public partners alike are needed to clarify the just and maximally effective use of satellites in humanitarian scenarios.

Maxar satellite imagery shows the aftermath of the explosion at the Port of Beirut on Aug. 5, 2020. MAXAR TECHNOLOGIES

A Lens to the Future

Geospatial data is providing a critical tool for monitoring and responding to natural disasters, an essential perspective on the impacts of climate change, and an invaluable lens on conflict and human behavior. But more can be done. The proliferation of satellite- and aerial-based systems equipped with upgraded sensing capabilities can expand the scope of coverage and relevant applications; machine learning technologies and advancements in data processing can speed use and lower costs; and mindful cooperation between partners can help ensure that needed data is in the right hands when it can make the greatest impact. Continued investment and conscientious public-private efforts to make services more accessible will ensure that geospatial data achieves its maximum potential and primary goal—enhancing security and improving lives on the ground.

Produced by:

FP Analytics

Underwritten by:


  1. Norman Kerle et al., “UAV-Based Structural Damage Mapping: A Review,” International Journal of Geo-Information 9, no. 14 (December 2019), doi:10.3390/ijgi9010014.

  2. Economist Intelligence Unit, “Improving disaster response efforts through data,” The Economist, n.d.,

  3. Lesley Bourns and Jessica Alexander, eds., Leaving No One Behind: Humanitarian Effectiveness in the Age of the Sustainable Development Goals (New York: UN Office for the Coordination of Humanitarian Affairs, 2015),

  4. Rémi Froment et al., Use of Earth Observation Satellites to Improve Effectiveness of Humanitarian Operations (Brussels: Centre for Research on the Epidemiology of Disasters, November 2020),

  5. Ibid.

  6. G. Le Cozannet et al., “Space-Based Earth Observations for Disaster Risk Management,” Surveys in Geophysics 41 (2020): 1209–35, doi:10.1007/s10712-020-09586-5.

  7. Secretary of the Air Force Public Affairs, “Eagle Vision program highlights International and Total Force partnership,” U.S. Air Force, June 18, 2018,

  8. World Bank, GFDRR, and ImageCat, 2010 Haiti Earthquake: Final Report (Washington, D.C.: n.d.),; and Kerle et al., “UAV-Based Structural Damage Mapping: A Review.”

  9. John A. Quinn et al., “Humanitarian applications of machine learning with remote-sensing data: review and case study in refugee settlement mapping,” Philosophical Transactions of the Royal Society A 376, no. 2128 (September 2018), doi:10.1098/rsta.2017.0363.

  10. Yifang Ban et al., “Near Real-Time Wildfire Progression Monitoring with Sentinel-1 SAR Time Series and Deep Learning,” Scientific Reports 10 (2020),

  11. Froment et al., Use of Earth Observation Satellites to Improve Effectiveness of Humanitarian Operations.

  12. Quinn et al., “Humanitarian applications of machine learning with remote-sensing data.”

  13. Froment et al., Use of Earth Observation Satellites to Improve Effectiveness of Humanitarian Operations.

  14. Ibid.

  15. Sonia I. Seneviratne et al., “Changes in climate extremes and their impacts on the natural physical environment,” in Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation, edited by  C.B. Field et al. (Cambridge, UK, and New York: Cambridge University Press, 2012), 109–230.

  16. Richard Conniff, “Eyes on Nature: How Satellite Imagery Is Transforming Conservation Science,” Yale Environment 360, June 22, 2017,; and Hua-Dong Guo et al., “Earth observation big data for climate change research,” Advances in Climate Change Research 6, no. 2 (June 2015): 108–17, doi:10.1016/j.accre.2015.09.007.

  17. Roz Pidcock, “Explainer: how surface and satellite temperature records compare,” CarbonBrief, February 1, 2016,

  18. Aditya Chaturvedi, “How satellite imagery is crucial for monitoring climate change,” Geospatial World, January 30, 2020,

  19. Jeff St. John, “Climate TRACE: Using Satellites and Machine Learning to Pinpoint Global Emissions,” Greentech Media, July 15, 2020,

  20. Al Tadros, “What is TEMPO and What Will It Do?” Maxar, September 6, 2019,

  21. “Mission Overview,” TEMPO, 2015,; and St. John, “Climate TRACE.”

  22. Kevin Robert Gurney et al., “Under-reporting of greenhouse gas emissions in U.S. cities,” Nature Communications 12 (2021),

  23. “Mission Overview,” TEMPO, 2015,

  24. Conniff, “Eyes on Nature.”

  25. Jeffrey Kluger, “The Great Crack-Up,” Time Magazine, January 29, 2018,

  26. Quinn et al., “Humanitarian applications of machine learning with remote-sensing data.”

  27. Nina von Uexkull et al., “Civil conflict sensitivity to growing-season drought,” PNAS 113, no. 44 (November 2016): 12391–196, doi:10.1073/pnas.1607542113; Matthew Melino and Heather A. Conley, “The Ice Curtain: Russia’s Arctic Military Presence,” Center for Strategic and International Studies, March 26, 2020,; and FP Analytics, “Arctic Competition Part II: Military Buildup and Great Power Competition,” FP Insider, December 20, 2020,

  28. Mattia Marconcini, “Outlining where humans live, the World Settlement Footprint 2015,” Scientific Data 7 (2020),

  29. A.J. Marx and T.V. Loboda, “Landsat-based early warning system to detect the destruction of villages in Darfur, Sudan,” Remote Sensing if Environment 136 (September 2013): 126–34, doi:10.1016/j.rse.2013.05.006; David Anderson and Emma Lochery, Violence and Exodus in Kenya’s Rift Valley, 2008: Predictable and Preventable?” Journal of Eastern African Studies 2, no. 2 (May 2008): 328–43, doi:10.1080/17531050802095536; and Andrew Marx, Richard Windisch, and Jong Su Kim, “Detecting village burnings with high-cadence smallsats: A case-study in the Rakhine State of Myanmar,” Remote Sensing Applications: Society and Environment 14 (April 2019): 119–25, doi:10.1016/j.rsase.2019.02.008.

  30. Ram Avtar et al., “Remote Sensing for International Peace and Security: Its Role and Implications,” Remote Sensing 13 (2021), doi:103390/rs13030439.

  31. Susan Wolfinbarger, Jonathan Drake, and Eric Ashcroft, Monitoring Border Conflicts with Satellite Imagery: A Handbook for Practitioners (Washington, D.C.: American Association for the Advancement of Science, 2015),; and Spencer Ackerman et al., “Yazidis still stranded on Mount Sinjar: ‘We need weapons now more than food or water’,” The Guardian, August 27, 2014,

  32. Brian Katz, Seth G. Jones, Catrina Doxsee, and Nicholas Harrington, “Moscow’s Mercenary Wars,” Center for Strategic and International Studies, September 2020,

  33. “When Mapping Saves Lives: Supporting the Ebola Response in DRC,” Maxar, June 17, 2018,; “Responding to the Ebola Crisis: Helping combat the Ebola epidemic using geospatial information,” Maxar, accessed May 7, 2020,; and Froment et al., Use of Earth Observation Satellites to Improve Effectiveness of Humanitarian Operations.

  34. “Case Study: Satellite imagery in conflict-affected areas,” World Food Programme, April 2020,

  35. Tomaz Logar et al., “PulseSatellite: A tool using human-AI feedback loops for satellite image analysis in humanitarian contexts,” United Nations Global Pulse, January 29, 2020,

  36. Ariel Sabar, “The U.N. Uses Satellite to Track Humanitarian Crises Around the World,” Smithsonian Magazine, May 2015,

  37. Mark Dorman et al., “China’s frontier of fear,” Australian Broadcasting Corporation, October 31, 2018,; and Megha Rajagopalan, Alison Killing, and Christo Buschek, “Built to Last,” Buzzfeed News, August 27, 2020,

  38. Maksymilian Czuperski et al., Hiding in Plain Sight: Putin’s War in Ukraine (Washington, D.C.: Atlantic Council, 2015),

  39. “Satellite Data: What Spatial Resolution Is Enough?,” Earth Observing System, December 4, 2019,

  40. Lingli Zhu et al., “A Review Remote Sensing Sensors,” in Multi-purposeful Application of Geospatial Data, edited by Rustam B. Rustamov (London: IntechOpen, 2018),

  41. Quinn et al., “Humanitarian applications of machine learning with remote-sensing data.”

  42. Giancarlo Santilli et al., “CubeSat constellations for disaster management in remote areas,” Acta Astronautica 145 (April 2018): 11–17, doi:10.1016/j.actaastro.2017.12.050.

  43. Maxar, “Because it Takes a Legion,” accessed May 14, 2021,

  44. Daniele Ventura et al., “Unmanned Aerial Systems (UASs) for Environmental Monitoring: A Review with Applications in Coastal Habitats,” in Aerial Robots: Aerodynamics, Control and Applications, edited by Omar D. Lopez Mejia (London: IntechOpen, September 2017), doi:10.5772/intechopen.69598; and Joseph Magnotta, “Use of Drones in GIS,” GIS Lounge, January 5, 2015,

  45. Quinn et al., “Humanitarian applications of machine learning with remote-sensing data”; and Emilie Bruzelius et al., “Satellite images and machine learning can identify remote communities to facilitate access to health services,” Journal of the American Medical Informatics Association 26, no. 8–9 (August/September 2019): 806–12, doi:10.1093/jamia/ocz111.

  46. Quinn et al., “Humanitarian applications of machine learning with remote-sensing data.”

  47. Jeff Carr, “Sending Data from Space to Amazon S3 in Less than a Minute,” Maxar Technologies, November 27, 2018,; and “How much is 1 TB of storage?” Dropbox,

  48. Froment et al., Use of Earth Observation Satellites to Improve Effectiveness of Humanitarian Operations.

  49. Gabriel Popkin, “Technology and satellite companies open up a world of data,” Nature, May 29, 2018,

  50. “JPT Members,” Sentinel Asia,; and “About Sentinel Asia,” Sentinel Asia,

  51. Froment et al., Use of Earth Observation Satellites to Improve Effectiveness of Humanitarian Operations.

  52. Quinn et al., “Humanitarian applications of machine learning with remote-sensing data.”

  53. Livingston and Drake, “We tried to save 150 people in Aleppo from 5,000 miles away.”