Fusing remote and social sensing data for flood impact mapping
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| Udgivet i: | AI Magazine vol. 45, no. 4 (Dec 1, 2024), p. 486-502 |
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| Hovedforfatter: | |
| Andre forfattere: | , , , , |
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John Wiley & Sons, Inc.
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| Online adgang: | Citation/Abstract Full Text Full Text - PDF |
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MARC
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| 024 | 7 | |a 10.1002/aaai.12196 |2 doi | |
| 035 | |a 3275470976 | ||
| 045 | 2 | |b d20241201 |b d20250331 | |
| 084 | |a 15019 |2 nlm | ||
| 100 | 1 | |a Akhtar, Zainab |u Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar | |
| 245 | 1 | |a Fusing remote and social sensing data for flood impact mapping | |
| 260 | |b John Wiley & Sons, Inc. |c Dec 1, 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 610 | 4 | |a United Nations Development Programme United Nations--UN | |
| 651 | 4 | |a Pakistan | |
| 653 | |a Disaster relief | ||
| 653 | |a Local authorities | ||
| 653 | |a Humanitarianism | ||
| 653 | |a Data sources | ||
| 653 | |a Computer vision | ||
| 653 | |a Mapping | ||
| 653 | |a Natural disasters | ||
| 653 | |a Organizational effectiveness | ||
| 653 | |a Remote sensing | ||
| 653 | |a Spatial data | ||
| 653 | |a Emergency preparedness | ||
| 653 | |a Development programs | ||
| 653 | |a Situational awareness | ||
| 653 | |a Data | ||
| 653 | |a Floods | ||
| 653 | |a Data processing | ||
| 653 | |a Deep learning | ||
| 653 | |a Flood damage | ||
| 653 | |a Flood mapping | ||
| 653 | |a Social networks | ||
| 653 | |a Disaster management | ||
| 653 | |a Hydrologic data | ||
| 653 | |a Damage | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Needs analysis | ||
| 653 | |a Infrastructure | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Truth | ||
| 653 | |a Natural language processing | ||
| 653 | |a Geographic information systems | ||
| 653 | |a Social | ||
| 700 | 1 | |a Qazi, Umair |u Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar | |
| 700 | 1 | |a El‐Sakka, Aya |u Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar | |
| 700 | 1 | |a Sadiq, Rizwan |u Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar | |
| 700 | 1 | |a Ofli, Ferda |u Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar | |
| 700 | 1 | |a Imran, Muhammad |u Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar | |
| 773 | 0 | |t AI Magazine |g vol. 45, no. 4 (Dec 1, 2024), p. 486-502 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3275470976/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3275470976/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3275470976/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |