Assessing On-the-Ground Disaster Impact Using Online Data Sources
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arXiv
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| Format: | Preprint |
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2025
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| author | Vishnubhatla, Saketh Jeong, Ujun Jiang, Bohan Sheth, Paras Tan, Zhen Raglin, Adrienne Liu, Huan |
| author_facet | Vishnubhatla, Saketh Jeong, Ujun Jiang, Bohan Sheth, Paras Tan, Zhen Raglin, Adrienne Liu, Huan |
| contents | Assessing the impact of a disaster in terms of asset losses and human casualties is essential for preparing effective response plans. Traditional methods include offline assessments conducted on the ground, where volunteers and first responders work together to collect the estimate of losses through windshield surveys or on-ground inspection. However, these methods have a time delay and are prone to different biases. Recently, various online data sources, including social media, news reports, aerial imagery, and satellite data, have been utilized to evaluate the impact of disasters. Online data sources provide real-time data streams for estimating the offline impact. Limited research exists on how different online sources help estimate disaster impact at a given administrative unit. In our work, we curate a comprehensive dataset by collecting data from multiple online sources for a few billion-dollar disasters at the county level. We also analyze how online estimates compare with traditional offline-based impact estimates for the disaster. Our findings provide insight into how different sources can provide complementary information to assess the disaster. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_11634 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Assessing On-the-Ground Disaster Impact Using Online Data Sources Vishnubhatla, Saketh Jeong, Ujun Jiang, Bohan Sheth, Paras Tan, Zhen Raglin, Adrienne Liu, Huan Machine Learning Assessing the impact of a disaster in terms of asset losses and human casualties is essential for preparing effective response plans. Traditional methods include offline assessments conducted on the ground, where volunteers and first responders work together to collect the estimate of losses through windshield surveys or on-ground inspection. However, these methods have a time delay and are prone to different biases. Recently, various online data sources, including social media, news reports, aerial imagery, and satellite data, have been utilized to evaluate the impact of disasters. Online data sources provide real-time data streams for estimating the offline impact. Limited research exists on how different online sources help estimate disaster impact at a given administrative unit. In our work, we curate a comprehensive dataset by collecting data from multiple online sources for a few billion-dollar disasters at the county level. We also analyze how online estimates compare with traditional offline-based impact estimates for the disaster. Our findings provide insight into how different sources can provide complementary information to assess the disaster. |
| title | Assessing On-the-Ground Disaster Impact Using Online Data Sources |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.11634 |