In disasters the physical and communications infrastructure are often damaged simultaneously: roads can become impassable, mobile networks can fail, and terrain can change within hours. Effective response therefore depends on obtaining fast, accurate information about which areas are reachable, where damage is worst, and where to send help first.
Help.NGO aerial imagery and AWS processing
The international humanitarian organization Help.NGO uses drone-captured aerial imagery to rapidly assess situations. The technology has been deployed in responses to Mexican hurricanes, wildfires in the Amazon region, the earthquakes in Turkey, and the war in Ukraine. Individual missions can produce multiple terabytes of imagery, and that volume of data is processed quickly using Amazon Web Services (AWS).
A key development is that the system works even with weak or unreliable internet connectivity: private 4G/5G networks and on-site AWS Outposts allow data analysis to begin at the disaster location. AI models can identify buildings, roads and other features in the imagery, assess damage and support hazard prediction. The stated aim is to provide incident commanders with usable information in hours rather than days.
HOT fills missing maps with volunteers and AI
The Humanitarian OpenStreetMap Team (HOT) addresses a different but related problem: many at-risk areas still lack adequate digital maps. Roads, buildings, hospitals and even entire settlements may be missing from databases, which can significantly complicate getting responders and aid deliveries to the right places after a disaster.
Founded after the 2010 Haitian earthquake, the Humanitarian OpenStreetMap Team has since supported mapping in more than 170 disaster situations. Its AWS-based system coordinates the work of an international volunteer community, and uses AI to help detect roads, buildings and other infrastructure from drone and satellite imagery. This approach speeds up a mapping process that was previously extremely labour intensive.
Common thread: fast, actionable information
Across the examples the advantage is not simply having more data, but turning that data into actionable information in a very short time. Local processing, private communications networks and AI-based recognition together help reduce disaster-response decision time from days to hours.
