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Fusing remote and social sensing data for flood impact mapping

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Argitaratua izan da:AI Magazine vol. 45, no. 4 (Dec 1, 2024), p. 486-502
Egile nagusia: Akhtar, Zainab
Beste egile batzuk: Qazi, Umair, El‐Sakka, Aya, Sadiq, Rizwan, Ofli, Ferda, Imran, Muhammad
Argitaratua:
John Wiley & Sons, Inc.
Gaiak:
United Nations Development Programme United Nations > UN
Pakistan
Disaster relief
Local authorities
Humanitarianism
Data sources
Computer vision
Mapping
Natural disasters
Organizational effectiveness
Remote sensing
Spatial data
Emergency preparedness
Development programs
Situational awareness
Data
Floods
Data processing
Deep learning
Flood damage
Flood mapping
Social networks
Disaster management
Hydrologic data
Damage
Statistical analysis
Needs analysis
Infrastructure
Artificial intelligence
Truth
Natural language processing
Geographic information systems
Social
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Laburpena: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 nontraditional 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 various statistical analyses using official ground‐truth data, showcasing its strong performance and explanatory power of integrating multiple data sources. 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.
ISSN:0738-4602
2371-9621
DOI:10.1002/aaai.12196
Baliabidea:ABI/INFORM Global