Spatiotemporal Typhoon Damage Assessment
A Multi-Task Learning Method for Location Extraction and Damage Identification from Social Media Texts
Bibliographic Data
| ID | 22033489 |
|---|---|
| Authors | Liwei Zou (0000-0003-3423-2443, Sun Yat-sen University), Zhi He (0000-0001-7567-8287, Sun Yat-sen University, corresponding author), Xianwei Wang (0009-0007-4801-1736, Sun Yat-sen University), Yutian Liang (Sun Yat-sen University) |
| Year | 2025 |
| Volume | 14 |
| Issue | 5 |
| Pages | 189 |
| Publication date | 2025-04-30 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi14050189 |
| OpenAlex | W4410008048 |
| Language | EN |
| Citations received | 1 |
| References cited | 46 |
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
Biology · Chromatography · Geography · Meteorology · Social media · Typhoon · World Wide Web · Chemistry · Computer Science · Disaster Management and Resilience · Engineering · Seismology and Earthquake Studies · Tropical and Extratropical Cyclones Research · Artificial Intelligence · Ecology
Relief Supply-Demand Estimation Based on Social Media in Typhoon Disasters Using Deep Learning and a Spatial Information Diffusion Model
Spatiotemporal Evolution of the Online Social Network after a Natural Disaster
Do typhoon disasters foster climate change concerns? Evidence from public discussions on social media in China
Assessing the effectiveness of existing early warning systems and emergency preparedness towards reducing cyclone-induced losses in the Sundarban Biosphere Region, India
Temporal and Spatial Evolution and Influencing Factors of Public Sentiment in Natural Disasters—A Case Study of Typhoon Haiyan
| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 1 |