Enhancing Disaster Situation Awareness Through Multimodal Social Media Data: Evidence from Typhoon Haikui
Emergency situation awareness during sudden natural disasters presents significant challenges. Traditional methods, characterized by low spatial and temporal resolution as well as coarse granularity, often fail to comprehensively capture disaster situations. However, social media platforms, as a vit...
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| Format: | Article |
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/1/465 |
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| author | Songfeng Gao Tengfei Yang Yuning Xu Naixia Mou Xiaodong Wang Hao Huang |
| author_facet | Songfeng Gao Tengfei Yang Yuning Xu Naixia Mou Xiaodong Wang Hao Huang |
| author_sort | Songfeng Gao |
| collection | DOAJ |
| description | Emergency situation awareness during sudden natural disasters presents significant challenges. Traditional methods, characterized by low spatial and temporal resolution as well as coarse granularity, often fail to comprehensively capture disaster situations. However, social media platforms, as a vital source of social sensing, offer significant potential to supplement disaster situational awareness. This paper proposes an innovative framework for disaster situation awareness based on multimodal data from social media to identify social media content related to typhoon disasters. Integrating text and image data from social media facilitates near real-time monitoring of disasters from the public perspective. In this study, Typhoon Haikui (Strong Typhoon No. 11 of 2023) was chosen as a case study to validate the effectiveness of the proposed method. We employed the ERNIE natural language processing model to complement the Deeplab v3+ deep learning image semantic segmentation model for extracting disaster damage information from social media. A spatial visualization analysis of the disaster-affected areas was performed by categorizing the damage types. Additionally, the Geodetector was used to investigate spatial heterogeneity and its underlying factors. This approach allowed us to analyze the spatiotemporal patterns of disaster evolution, enabling rapid disaster damage assessment and facilitating emergency response efforts. The results show that the proposed method significantly enhances situational awareness by effectively identifying different types of damage information from social sensing data. |
| format | Article |
| id | doaj-art-364cbb0ef9bd433da199d10bec76afa8 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-364cbb0ef9bd433da199d10bec76afa82025-01-10T13:15:39ZengMDPI AGApplied Sciences2076-34172025-01-0115146510.3390/app15010465Enhancing Disaster Situation Awareness Through Multimodal Social Media Data: Evidence from Typhoon HaikuiSongfeng Gao0Tengfei Yang1Yuning Xu2Naixia Mou3Xiaodong Wang4Hao Huang5School of Surveying and Urban Spatial Information, Henan University of Urban Construction, Pingdingshan 467000, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaCollege of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, ChinaSchool of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471000, ChinaCollege of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, ChinaEmergency situation awareness during sudden natural disasters presents significant challenges. Traditional methods, characterized by low spatial and temporal resolution as well as coarse granularity, often fail to comprehensively capture disaster situations. However, social media platforms, as a vital source of social sensing, offer significant potential to supplement disaster situational awareness. This paper proposes an innovative framework for disaster situation awareness based on multimodal data from social media to identify social media content related to typhoon disasters. Integrating text and image data from social media facilitates near real-time monitoring of disasters from the public perspective. In this study, Typhoon Haikui (Strong Typhoon No. 11 of 2023) was chosen as a case study to validate the effectiveness of the proposed method. We employed the ERNIE natural language processing model to complement the Deeplab v3+ deep learning image semantic segmentation model for extracting disaster damage information from social media. A spatial visualization analysis of the disaster-affected areas was performed by categorizing the damage types. Additionally, the Geodetector was used to investigate spatial heterogeneity and its underlying factors. This approach allowed us to analyze the spatiotemporal patterns of disaster evolution, enabling rapid disaster damage assessment and facilitating emergency response efforts. The results show that the proposed method significantly enhances situational awareness by effectively identifying different types of damage information from social sensing data.https://www.mdpi.com/2076-3417/15/1/465disaster informaticssocial sensingcomputer visionerniespatiotemporal situational awareness |
| spellingShingle | Songfeng Gao Tengfei Yang Yuning Xu Naixia Mou Xiaodong Wang Hao Huang Enhancing Disaster Situation Awareness Through Multimodal Social Media Data: Evidence from Typhoon Haikui Applied Sciences disaster informatics social sensing computer vision ernie spatiotemporal situational awareness |
| title | Enhancing Disaster Situation Awareness Through Multimodal Social Media Data: Evidence from Typhoon Haikui |
| title_full | Enhancing Disaster Situation Awareness Through Multimodal Social Media Data: Evidence from Typhoon Haikui |
| title_fullStr | Enhancing Disaster Situation Awareness Through Multimodal Social Media Data: Evidence from Typhoon Haikui |
| title_full_unstemmed | Enhancing Disaster Situation Awareness Through Multimodal Social Media Data: Evidence from Typhoon Haikui |
| title_short | Enhancing Disaster Situation Awareness Through Multimodal Social Media Data: Evidence from Typhoon Haikui |
| title_sort | enhancing disaster situation awareness through multimodal social media data evidence from typhoon haikui |
| topic | disaster informatics social sensing computer vision ernie spatiotemporal situational awareness |
| url | https://www.mdpi.com/2076-3417/15/1/465 |
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