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

Authors

  • Zainab Akhtar Qatar Computing Research Institute, HBKU, Doha, Qatar
  • Umair Qazi Qatar Computing Research Institute, HBKU, Doha, Qatar
  • Aya El-Sakka Qatar Computing Research Institute, HBKU, Doha, Qatar
  • Rizwan Sadiq Central Asian University, Tashkent, Uzbekistan
  • Ferda Ofli Qatar Computing Research Institute, HBKU, Doha, Qatar
  • Muhammad Imran Qatar Computing Research Institute, HBKU, Doha, Qatar

DOI:

https://doi.org/10.1609/aaai.v38i21.30305

Keywords:

Social Media , Information Extraction , Information Fusion , Track: Deployed Applications

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.

Published

2024-03-24

How to Cite

Akhtar, Z., Qazi, U., El-Sakka, A., Sadiq, R., Ofli, F., & Imran, M. (2024). Flood Insights: Integrating Remote and Social Sensing Data for Flood Exposure, Damage, and Urgent Needs Mapping. Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 22716-22724. https://doi.org/10.1609/aaai.v38i21.30305

Issue

Section

IAAI Technical Track on Deployed Highly Innovative Applications of AI