Abstract
The absence of comprehensive situational awareness information poses a significant challenge for humanitarian organizations during their response efforts. We present Flood Insights, an end-to-end system that ingests data from multiple non-traditional data sources such as remote sensing, social sensing, and geospatial data. We employ state-of-the-art natural language processing and computer vision models to identify flood exposure, ground-level damage and flood reports, and most importantly, urgent needs of affected people. We deploy and test the system during a recent real-world catastrophe, the 2022 Pakistan floods, to surface critical situational and damage information at the district level. We validated the system's effectiveness through geographic regression analysis using official ground-truth data, showcasing its strong performance and explanatory power. Moreover, the system was commended by the United Nations Development Programme stationed in Pakistan, as well as local authorities, for pinpointing hard-hit districts and enhancing disaster response.
| Original language | English |
|---|---|
| Pages (from-to) | 22716-22724 |
| Number of pages | 9 |
| Journal | Proceedings of the AAAI Conference on Artificial Intelligence |
| Volume | 38 |
| Issue number | 21 |
| DOIs | |
| Publication status | Published - 25 Mar 2024 |
| Event | 38th AAAI Conference on Artificial Intelligence, AAAI 2024 - Vancouver, Canada Duration: 20 Feb 2024 → 27 Feb 2024 |
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