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Assessing traffic road damage post-disaster is crucial for emergency decision-making and disaster management. Traditional ground observation methods for evaluating traffic road damage are limited by the timeliness and coverage of data updates. Relying solely on these methods does not adequately support rapid assessment and emergency management during extreme natural disasters. Social media, a major source of big data, can effectively address these limitations by providing more timely and comprehensive disaster information. Motivated by this, we utilized multi-source heterogeneous data to assess the damage to traffic roads under extreme conditions and established a new framework for evaluating traffic roads in cities prone to flood disasters caused by rainstorms. The approach involves several steps: First, the surface area affected by precipitation is extracted using a threshold method constrained by confidence intervals derived from microwave remote sensing images. Second, disaster information is collected from the Sina Weibo platform, where social media information is screened and cleaned. A quantification table for road traffic loss assessment was defined, and a social media disaster information classification model combining text convolutional neural networks and attention mechanisms (TextCNN-Attention disaster information classification) was proposed. Finally, traffic road information on social media is matched with basic geographic data, the classification of traffic road disaster risk levels is visualized, and the assessment of traffic road disaster levels is completed based on multi-source heterogeneous data. Using the \u201c7.20\u201d rainstorm event in Henan Province as an example, this research categorizes the disaster\u2019s impact on traffic roads into five levels\u2014particularly severe, severe, moderate, mild, and minimal\u2014as derived from remote sensing image monitoring and social media information analysis. The evaluation framework for flood disaster traffic roads based on multi-source heterogeneous data provides important data support and methodological support for enhancing disaster management capabilities and systems.<\/jats:p>","DOI":"10.3390\/ijgi13100369","type":"journal-article","created":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T08:53:11Z","timestamp":1729500791000},"page":"369","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Feasibility of Emergency Flood Traffic Road Damage Assessment by Integrating Remote Sensing Images and Social Media Information"],"prefix":"10.3390","volume":"13","author":[{"given":"Hong","family":"Zhu","sequence":"first","affiliation":[{"name":"College of Ecology and Environment, Institute of Disaster Prevention, Beijing 101601, China"}]},{"given":"Jian","family":"Meng","sequence":"additional","affiliation":[{"name":"School of Earth Sciences and Engineering, Institute of Disaster Prevention, Beijing 101601, China"},{"name":"Beijing Disaster Prevention Science and Technology, Co., Ltd., Beijing 101100, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1449-7671","authenticated-orcid":false,"given":"Jiaqi","family":"Yao","sequence":"additional","affiliation":[{"name":"Hebei Key Laboratory of Resource and Environmental Disaster Mechanism and Risk Monitoring, Sanhe 065201, China"},{"name":"Academy of Eco-Civilization Development for Jing-Jin-Ji Megalopolis, Tianjin Normal University, Tianjin 300387, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-6129-3129","authenticated-orcid":false,"given":"Nan","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Geography and Remote Sensing, Hohai University, Nanjing 210024, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"118787","DOI":"10.1016\/j.jenvman.2023.118787","article-title":"Dynamic risk assessment of urban flood disasters based on functional area division\u2014A case study in Shenzhen, China","volume":"345","author":"Wang","year":"2023","journal-title":"J. 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