Every day, millions of images are captured from space by an ever-growing number of satellites. In humanitarian crises or conflict areas, accurate and timely satellite image analysis is key to supporting critical operations on the ground. Use cases include monitoring population displacement, settlement mapping, damage assessment, fire detection associated with human rights violations, damage to transportation networks, flood assessment and identifying the direct impact of earthquakes, volcanoes, cyclones and landslides. 

This type of analysis is traditionally done by human analysts who spent hours in front of satellite images, manually counting and classifying structures and other elements. There are now some pay-as-you-go services which also offer automated options although these are often directed towards specific use cases, or do not have the mandate to provide information on critical areas relevant to the UN. 

PulseSatellite allows our UN colleagues to get the most out of earth imagery by putting artificial intelligence to work. There are three models loaded into the system – one that allows users to map structures in refugee settlements; a roof density detection model that can be used for example, to infer the type of neighbourhood by looking at the size and proximity of its roofs; and a flood mapping model.

Our impact

PulseSatellite reduces the time needed for UN users to acquire, process, and load satellite imagery into machine learning models, making it easier for users to take advantage of these automated tools. PulseSatellite also offers human-in-the-loop capabilities for improving these models, allowing users to correct them and increase their performance over time. We are working to extend PulseSatellite’s capabilities to include damage detection as part of the Data Insights for Social & Humanitarian Action (Disha) initiative.