TY - JOUR
T1 - Flood Insights
T2 - 38th AAAI Conference on Artificial Intelligence, AAAI 2024
AU - Akhtar, Zainab
AU - Qazi, Umair
AU - El-Sakka, Aya
AU - Sadiq, Rizwan
AU - Ofli, Ferda
AU - Imran, Muhammad
N1 - Publisher Copyright:
Copyright © 2024, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2024/3/25
Y1 - 2024/3/25
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85189643932&partnerID=8YFLogxK
U2 - 10.1609/aaai.v38i21.30305
DO - 10.1609/aaai.v38i21.30305
M3 - Conference article
AN - SCOPUS:85189643932
SN - 2159-5399
VL - 38
SP - 22716
EP - 22724
JO - Proceedings of the AAAI Conference on Artificial Intelligence
JF - Proceedings of the AAAI Conference on Artificial Intelligence
IS - 21
Y2 - 20 February 2024 through 27 February 2024
ER -