Spatiotemporal Typhoon Damage Assessment: A Multi-Task Learning Method for Location Extraction and Damage Identification from Social Media Texts
Typhoons are among the most destructive natural phenomena, posing significant threats to human society. Therefore, accurate damage assessment is crucial for effective disaster management and sustainable development. While social media texts have been widely used for disaster analysis, most current s...
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MDPI AG
2025-04-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/14/5/189 |
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| author | Liwei Zou Zhi He Xianwei Wang Yutian Liang |
| author_facet | Liwei Zou Zhi He Xianwei Wang Yutian Liang |
| author_sort | Liwei Zou |
| collection | DOAJ |
| description | Typhoons are among the most destructive natural phenomena, posing significant threats to human society. Therefore, accurate damage assessment is crucial for effective disaster management and sustainable development. While social media texts have been widely used for disaster analysis, most current studies tend to neglect the geographic references and primarily focus on single-label classification, which limits the real-world utility. In this paper, we propose a multi-task learning method that synergizes the tasks of location extraction and damage identification. Using Bidirectional Encoder Representations from Transformers (BERT) with auxiliary classifiers as the backbone, the framework integrates a toponym entity recognition model and a multi-label classification model. Novel toponym-enhanced weights are designed as a bridge to generate augmented text representations for both tasks. Experimental results show high performance, with F1-scores of 0.891 for location extraction and 0.898 for damage identification, representing improvements of 4.3% and 2.5%, respectively, over single-task and deep learning baselines. A case study of three recent typhoons (In-fa, Chaba, and Doksuri) that hit China’s coastal regions reveals the spatial distribution and temporal pattern of typhoon damage, providing actionable insights for disaster management and resource allocation. This framework is also adaptable to other disaster scenarios, supporting urban resilience and sustainable development. |
| format | Article |
| id | doaj-art-61fc591d6a4e4b1cb78c8a79dcf4a971 |
| institution | OA Journals |
| issn | 2220-9964 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj-art-61fc591d6a4e4b1cb78c8a79dcf4a9712025-08-20T02:33:54ZengMDPI AGISPRS International Journal of Geo-Information2220-99642025-04-0114518910.3390/ijgi14050189Spatiotemporal Typhoon Damage Assessment: A Multi-Task Learning Method for Location Extraction and Damage Identification from Social Media TextsLiwei Zou0Zhi He1Xianwei Wang2Yutian Liang3Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, ChinaSchool of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, ChinaTyphoons are among the most destructive natural phenomena, posing significant threats to human society. Therefore, accurate damage assessment is crucial for effective disaster management and sustainable development. While social media texts have been widely used for disaster analysis, most current studies tend to neglect the geographic references and primarily focus on single-label classification, which limits the real-world utility. In this paper, we propose a multi-task learning method that synergizes the tasks of location extraction and damage identification. Using Bidirectional Encoder Representations from Transformers (BERT) with auxiliary classifiers as the backbone, the framework integrates a toponym entity recognition model and a multi-label classification model. Novel toponym-enhanced weights are designed as a bridge to generate augmented text representations for both tasks. Experimental results show high performance, with F1-scores of 0.891 for location extraction and 0.898 for damage identification, representing improvements of 4.3% and 2.5%, respectively, over single-task and deep learning baselines. A case study of three recent typhoons (In-fa, Chaba, and Doksuri) that hit China’s coastal regions reveals the spatial distribution and temporal pattern of typhoon damage, providing actionable insights for disaster management and resource allocation. This framework is also adaptable to other disaster scenarios, supporting urban resilience and sustainable development.https://www.mdpi.com/2220-9964/14/5/189typhoon damage assessmentsocial mediamulti-task learninglocation extractionspatiotemporal analysis |
| spellingShingle | Liwei Zou Zhi He Xianwei Wang Yutian Liang Spatiotemporal Typhoon Damage Assessment: A Multi-Task Learning Method for Location Extraction and Damage Identification from Social Media Texts ISPRS International Journal of Geo-Information typhoon damage assessment social media multi-task learning location extraction spatiotemporal analysis |
| title | Spatiotemporal Typhoon Damage Assessment: A Multi-Task Learning Method for Location Extraction and Damage Identification from Social Media Texts |
| title_full | Spatiotemporal Typhoon Damage Assessment: A Multi-Task Learning Method for Location Extraction and Damage Identification from Social Media Texts |
| title_fullStr | Spatiotemporal Typhoon Damage Assessment: A Multi-Task Learning Method for Location Extraction and Damage Identification from Social Media Texts |
| title_full_unstemmed | Spatiotemporal Typhoon Damage Assessment: A Multi-Task Learning Method for Location Extraction and Damage Identification from Social Media Texts |
| title_short | Spatiotemporal Typhoon Damage Assessment: A Multi-Task Learning Method for Location Extraction and Damage Identification from Social Media Texts |
| title_sort | spatiotemporal typhoon damage assessment a multi task learning method for location extraction and damage identification from social media texts |
| topic | typhoon damage assessment social media multi-task learning location extraction spatiotemporal analysis |
| url | https://www.mdpi.com/2220-9964/14/5/189 |
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