Using AI and satellite imagery for disaster insights
Dec 1, 2018
Summary
Introduced a framework using CNNs on satellite imagery to identify disaster-affected areas by detecting changes in human-made features (roads, buildings) between pre- and post-disaster snapshots. Developed a Disaster Impact Index (DII) to quantify affected regions, correlating strongly with actual disaster impact in test cases (Hurricane Harvey, Tubbs Fire). This method aims to provide faster and more accurate disaster mapping information to rescue workers.
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