| Issue |
ITM Web Conf.
Volume 84, 2026
2026 International Conference on Advent Trends in Computational Intelligence and Data Science (ATCIDS 2026)
|
|
|---|---|---|
| Article Number | 03022 | |
| Number of page(s) | 12 | |
| Section | Large Language Models, Generative AI, and Multimodal Learning | |
| DOI | https://doi.org/10.1051/itmconf/20268403022 | |
| Published online | 06 April 2026 | |
Review of Geographic Information Technology Applications in Disaster Prevention and Mitigation for Urban Infrastructure
Resource College, Shandong University of Science and Technology, Taian, China, 271021
* Corresponding author’s email: This email address is being protected from spambots. You need JavaScript enabled to view it.
Abstract
Against the backdrop of global urbanization and climate change, traditional disaster prevention and mitigation models struggle to address the increasingly complex risks to urban infrastructure. This paper reviews the applications of Global Positioning System (GPS), Geographic Information System (GIS), and Remote Sensing (RS)in urban disaster management. It illustrates how these technologies complement each other through point-based positioning, spatial analysis, and wide-area monitoring across the entire disaster lifecycle. Research indicates that GPS provides precise spatiotemporal reference points, GIS enables multi-source information integration and analysis, and RS facilitates dynamic monitoring. The deep integration of these technologies constructs an intelligent closed-loop management system covering risk early warning, emergency response, and post-disaster recovery. This system significantly enhances the proactivity, accuracy, and systematic capacity of disaster management, thereby promoting a shift from reactive to proactive prevention and control. This study provides both a theoretical foundation and practical pathways for building resilient cities and intelligent disaster prevention and mitigation systems.
© The Authors, published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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