Flood Insights: Integrating Remote and Social Sensing Data for Flood Exposure, Damage, and Urgent Needs Mapping

Zainab Akhtar, Umair Qazi, Aya El-Sakka, Rizwan Sadiq, Ferda Ofli, Muhammad Imran

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)

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 languageEnglish
Pages (from-to)22716-22724
Number of pages9
JournalProceedings of the AAAI Conference on Artificial Intelligence
Volume38
Issue number21
DOIs
Publication statusPublished - 25 Mar 2024
Event38th AAAI Conference on Artificial Intelligence, AAAI 2024 - Vancouver, Canada
Duration: 20 Feb 202427 Feb 2024

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