AI Improves Disaster Preparedness With Accurate World Maps

LAS VEGAS — Intel has developed AI models to identify geographical features from satellite imagery for the creation of accurate, up-to-date maps. The company has been working closely with the Red Cross on its Missing Maps project, which aims to create maps for areas of the developing world to improve disaster preparedness. Many areas of the developing world do not have up-to-date maps, which means that aid organizations can struggle to work efficiently in the event of natural disasters or epidemics.

At present, Missing Maps uses a team of volunteers to go though satellite images and identify roads, towns, bridges, and other infrastructure. The volunteers manually update an open-source map called Open Street Map, which is laborious and time-consuming.

Intel’s AI Lab, in collaboration with Mila and CrowdAI, developed an image-segmentation model and used it to identify unmapped bridges in Uganda from satellite pictures. Object-detection approaches were discounted due to performance in favor of segmentation. Bridges were selected as a trial feature because they are critical infrastructure and are particularly vulnerable to natural disasters such as floods. Seventy previously unmapped bridges were discovered by the system; the Ugandan National Society can use this data to better plan evacuation and aid-delivery routes.

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