Version 1
: Received: 15 May 2023 / Approved: 17 May 2023 / Online: 17 May 2023 (07:44:02 CEST)
Version 2
: Received: 5 June 2023 / Approved: 6 June 2023 / Online: 6 June 2023 (10:26:36 CEST)
Wu, Y.; Chen, Y.; Zhang, R.; Cui, Z.; Liu, X.; Zhang, J.; Wang, M.; Wu, Y. A Spatial Information Extraction Method Based on Multi-Modal Social Media Data: A Case Study on Urban Inundation. ISPRS Int. J. Geo-Inf.2023, 12, 368.
Wu, Y.; Chen, Y.; Zhang, R.; Cui, Z.; Liu, X.; Zhang, J.; Wang, M.; Wu, Y. A Spatial Information Extraction Method Based on Multi-Modal Social Media Data: A Case Study on Urban Inundation. ISPRS Int. J. Geo-Inf. 2023, 12, 368.
Wu, Y.; Chen, Y.; Zhang, R.; Cui, Z.; Liu, X.; Zhang, J.; Wang, M.; Wu, Y. A Spatial Information Extraction Method Based on Multi-Modal Social Media Data: A Case Study on Urban Inundation. ISPRS Int. J. Geo-Inf.2023, 12, 368.
Wu, Y.; Chen, Y.; Zhang, R.; Cui, Z.; Liu, X.; Zhang, J.; Wang, M.; Wu, Y. A Spatial Information Extraction Method Based on Multi-Modal Social Media Data: A Case Study on Urban Inundation. ISPRS Int. J. Geo-Inf. 2023, 12, 368.
Abstract
With the prevalence and evolution of social media platforms, social media data have emerged as a crucial source for obtaining spatio-temporal information about various urban events. Providing accurate spatio-temporal information of these events enhances the capacities of urban management and emergency response. However, existing research mostly focuses on the textual content while mining this spatio-temporal information, often neglecting data from other modalities like images and videos.To address this, our study introduces a novel method for extracting spatio-temporal information from multi-modal social media data (MIST-SMMD), serving as a valuable supplement to current urban event monitoring methods. Leveraging deep learning and Geographic Information System (GIS) technologies, we extract spatio-temporal information from large-scale, multi-modal Weibo data about urban waterlogging events at both coarse and fine granularities.Through an in-depth experimental evaluation of the “July 20 Zhengzhou extreme rainstorm” event, the results show that in coarse-grained spatial information extraction solely using textual data, our method achieves a Spatial Precision of 87.54% within a 60m range and 100% Spatial Precision within a 201m range. In the fine-grained spatial information extraction, by incorporating other modalities such as images and videos, the Spatial Error saw a significant improvement, with MAESE increasing by 95.53% and RMSESE by 93.62%.These outcomes illustrate the capability of the MIST-SMMD method in extracting spatio-temporal information of urban events at both coarse and fine granularities. They also confirm the notable advantage of multi-modal data in enhancing the accuracy of spatial information extraction.
Keywords
multimodal data; social media; spatio-temporal information extraction; inundation
Subject
Environmental and Earth Sciences, Geography
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
