An Integration Framework of Remote Sensing and Social Media for Dynamic Post-Earthquake Impact Assessment
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Le résumé fourni par la source
Effective post-disaster management requires continuous and reliable monitoring of the evolving disaster situation. While remote sensing provides objective measurements of ground deformation, social media data offer dynamic insights into public perception and disaster progression. However, integrating these complementary data sources to achieve sustained monitoring of disaster remains a challenge. To address this, we propose a novel framework that combines Sentinel-1 SAR data with Sina Weibo posts to improve dynamic earthquake impact assessment. Physical damage was quantified using D-InSAR-derived deformation. Disaster-related locations were identified using a fine-tuned pre-trained language model, and public sentiment was inferred through prompt-based few-shot learning with a large language model. Spatiotemporal analysis was performed to examine the relationship between sentiment dynamics and varying levels of physical damage, followed by an analysis of topic transitions within regional semantic networks to compare discussion patterns across areas. A case study of the 2023 Jishishan earthquake demonstrates the framework’s capability to continuously track disaster evolution: regions experiencing severe physical damage exhibit clear concentrations of negative sentiment, whereas increases in positive sentiment coincide with areas where rescue operations are effectively underway. These findings indicate that integrating the two data sources improves continuous disaster monitoring and situational awareness, thereby supporting emergency response.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- An Integration Framework of Remote Sensing and Social Media for Dynamic Post-Earthquake Impact Assessment
- Date Crossref
- 13/12/2025
- Éditeur
- MDPI AG
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Chinese Academy of Sciences pays non établi dans la noticeOrganisme public
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Aerospace Information Research Institute pays non établi dans la noticeUniversité ou école supérieure
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Henan Polytechnic University pays non établi dans la noticeUniversité ou école supérieure
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Shandong University of Science and Technology pays non établi dans la noticeUniversité ou école supérieure
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School of Surveying and Land Information Engineering pays non établi dans la noticeUniversité ou école supérieure
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College of Geomatics pays non établi dans la noticeUniversité ou école supérieure
Chinese Academy of Sciences, Aerospace Information Research Institute et Henan Polytechnic University, avec 3 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.