Elevating Global Awareness

The Role of Satellite-Derived Geospatial Data for SDGs

Accelerating Achievement of the UN’s Sustainable Development Goals
Brought to you by Maxar Technologies

The Role of Satellite-Derived Geospatial Data for SDGs

SDG Special Section Brought to You by Maxar Technologies

For half a century, Earth observation (EO) satellites have provided consistent and accessible information about the state of our natural environment. Today, EO satellite imagery and the geospatial data it generates are helping us better understand the effects of climate change, monitor economic and social development, and protect human rights on a global scale. This data and its providers are quickly becoming a vital source for success in achieving the United Nations’ Sustainable Development Goals (SDGs).

Figure 1. Maxar satellite image of al-Hol refugee camp in Syria, taken on March 31, 2019.

Advanced EO capabilities provide essential data for accurately mapping remote areas, updating incomplete or outdated maps, especially in regions of rapid change. Not only does this assist in analyzing change, but it helps government and non-governmental organizations ensure they account for all those in need or at risk.

Innovations in EO satellite and geospatial data technology directly or indirectly support every single SDG. While in some cases, such as monitoring deforestation, it has been an established method for decades, the role of geospatial data has recently expanded into new use cases, such as providing valuable insights into gender inequality and disrupting human trafficking networks, thanks to advanced tradecraft and machine learning.

In 2017, the UN General Assembly adopted an indicator framework and assigned more defined targets to make the goals more concrete and measurable.

While a few countries are on track to meet SDG targets, many more are not and may or may not know it. How do these organizations measure and document success?

Traditional survey methods could cost as much as a quarter of a trillion
dollars over the lifetime of the SDG, take too long to be effective, and lead to incomplete or inconclusive results. Moreover, it is hard to gather consistent data across countries and regions.

Among satellite imagery and geospatial data providers, Maxar Technologies stands out for its innovative geospatial solutions and partnerships across all 17 SDGs. Maxar owns and operates the most sophisticated commercial satellite imagery constellation in orbit, continuing to feed its 100+ petabyte archive with more than 3 million sq km of high-resolution satellite imagery every day.

As the source of critical geospatial data—both current and historical—Maxar collaborates closely with partners in developing valuable analytics and derived datasets to help analysts detect, understand, and address change.

A key objective of Maxar’s purpose is making advanced geospatial data and insights more accessible—especially for those who need it but might not necessarily know how to use it. Its SDG partnerships are centered around empowering both analysts and volunteers to collaborate and collectively scrutinize imagery to tag important objects, features, or locations. The input of this human network is then validated and refined using advanced geospatial consensus algorithms that enable unprecedented accuracy at scale.


Target 1.1 By 2030, eradicate extreme poverty for all people everywhere, currently measured as people living on less than $1.25 (USD) a day

Target 1.5 By 2030, build the resilience of the poor and those in vulnerable situations and reduce their exposure to climate-related extreme events and other economic, social and environmental shocks and disasters

Indicator 1.1.1 Proportion of population below the international poverty line, by sex, age, employment status and geographical location (urban/rural)

Indicator 1.5.1 Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population

Indicator 1.5.2 Direct economic loss attributed to disasters in relation to global gross domestic product



EO satellites can detect changes in land cover, monitor water quality and identify pollutants, evaluate the health of a coral reef, or help assess how coastal zones are influenced by sea- level rise. Satellite images can be used to identify features of interest, such as agricultural land, forests, urban areas, roads, and water, and pixels can be classified into different crop types, or in a binary classification of forest or bare ground.

The rapid growth in the number of EO satellites has dramatically increased their coverage and refresh rate. Additionally, advancement in sensor technology has increased resolution and includes more bands, making them uniquely able to assist with SDG monitoring and implementation. For example, sensors in the infrared (IR) bands make it possible to understand such things as soil moisture and crop health, and radar satellites can capture imagery through clouds and haze.

Unlike the U.S. Landsat and the European Sentinel satellites, which steadily image the globe on a regular cadence, commercial satellites are dynamic and agile. For example, Maxar can task and point its constellation to collect images of areas where it thinks there will be more change, such as coastal areas and urban areas. It can also task its satellites quickly, to investigate suspicious areas or monitor a natural disaster.

Maxar’s constellation also features multispectral and radar capacity. Radar can “see” through clouds, haze, and smoke. Together, the constellation can use radar imagery to identify forest areas that are burning, then cue optical satellites to follow up once the air is clear. This allows for efficient post- event analysis or monitoring for ongoing events.

The advent of cloud computing and artificial intelligence (AI) has shifted the field of remote sensing from a data-scarce environment to a data-rich one. Satellite imagery is now also routinely analyzed in conjunction with “big data” from such sources as social media posts and call detail records.

AI is particularly important in disasters, where first responders and relief organizations are unable to analyze large streams of raw data. By comparing archival imagery with new imagery, AI can rapidly identify damaged structures, impassable roads, and priority needs. Crowdsourcing and machine learning can quickly validate and enrich the data to inform critical decisions.

Figure 2. Building footprints in Tanzania, relevant to SDG 6, Sustainable Development.


In some cases, such as measuring the extent of deforestation, the role of satellite imagery and geospatial technology has been established for decades and is intuitive. In other cases, such
as providing insights into gender inequality, its use is novel and surprising. At a minimum, it is essential to map areas that have never been mapped before, because you cannot get a vaccine or a fly net to a household if you don’t know it’s there.

Figure 3. Damage assessment of Abaco and Green Turtle Cay in the Bahamas after Hurricane Dorian. More than 3,000 buildings were assessed, with more than 1,100 visibly destroyed and 1,400 visibly damaged in Maxar satellite imagery. Building footprints provided by OpenStreetMap.

SDG partners are leveraging Maxar capabilities to:

    • Measure the amount of agricultural land, the length of shadows (which are a proxy for building height), and the density of buildings, roads, and cars so governments and stakeholders can estimate economic well-being and better understand the spatial distribution of poverty. (See Figure 2 of buildings in Tanzania.)
    • Tag permanent dwellings, temporary ones (such as tents), and herds of livestock in images to help researchers assess the level of food insecurity in a region and humanitarian organizations optimize their responses. (See refugee camp in Figure 1.)
    • Map areas at high risk for malaria, based on population density and nearby water sources, so health workers can determine the required number of life-saving mosquito nets and where to apply insecticides.
    • Map schools to address the infrastructure gaps that hinder educational opportunities, whether they be transportation, safety, Internet access, or other barriers.
    • Map residential areas, roads, streams, floodplains, and other relevant features, and combine this data with software inputs to run realistic natural disaster scenarios, which improve disaster preparedness planning and response plans.
    • Track the rapid growth in solar farms and solar panels on homes so decision makers can integrate this energy source into the grid and expose what motivates individuals to install solar arrays.
    • Identify brick kilns and illegal fishing boats, both routinely manned by forced labor, so law enforcement authorities can target them and free slaves.

Figure 4. Maxar satellite image of Green Turtle Cay in the Bahamas after Hurricane Dorian. Image taken September 5, 2019.


In the wake of major natural disasters, Maxar provides support for relief efforts by releasing free and open imagery of affected areas, including recently in the aftermath of Hurricane Dorian. (See Figures 3-4.) Through the Open Data Program, the company releases open imagery for select sudden onset major crisis events, including pre-event imagery, post-event imagery, and a crowdsourced damage assessment.

Maxar partners with dozens of national and international governmental agencies and NGOs to address specific crises.

  • In South Sudan, it partners with the Famine Early Warning System Network to study the causes and geographic distribution of hunger and malnutrition due to massive food and water shortages and to help plan optimal responses.
  • In Colombia, it uses real-time data from UNICEF to map schools, including those in the most remote regions.
  • In Tanzania, it uses street data from OpenStreetMap (OSM) to support flood resiliency and clean water.

Figure 5. Ecopia Building Footprints over the Central African Republic, extracted from Maxar satellite imagery by Maxar partner Ecopia.ai.