GeoAI can have transformative effects on disaster risk reduction programmes. For instance, these tools have been used to develop real-time crisis maps and filter geotagged data to pinpoint areas requiring urgent rescue operations. Additionally, image recognition techniques can assist in identifying disaster impacts such as collapsed buildings, while filling historical disaster data gaps. GeoAI tools have enabled governments and international organizations to develop early warning systems that can predict the spread of wildfires or floods and identify areas of vulnerability.
The United Nations has frequently employed GeoAI to assist in its disaster risk reduction efforts. For instance, the United Nations Satellite Centre (UNOSAT) launched FloodAI, an end-to-end pipeline that automatically downloads synthetic aperture radar imagery and uses a deep learning model to develop flood-extent maps and produce real-time flood updates. Additionally, UNOSAT used GeoAI to analyse satellite imagery and create damage assessment maps for the Türkiye-Syria earthquake in 2023, helping to identify collapsed buildings, plan rescue operations and allocate resources efficiently. The Philippines, commonly known as the typhoon alley of the world, has also utilized GeoAI in conjunction with drones and digital twins software to quickly identify the aftermath effects of typhoons and determine flood-prone areas. In November 2024, the United Nations established a Global Initiative on Resilience to Natural Hazards through AI Solutions, the successor to the recently completed Focus Group on AI for Natural Disaster Management.
