Innovations, applications, and future perspectives in geospatial information visualization for disaster response: insights from the 2023 Kahramanmaras Earthquake urban search and rescue operations

Martin Lyubomirov Ivanov () and Martin Plamenov Georgiev ()
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Martin Lyubomirov Ivanov: Fire Safety and Civil Protection Capital Directorate, Ministry of the Interior
Martin Plamenov Georgiev: National Association of Volunteers in the Republic of Bulgaria

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 19, No 19, 22787-22816

Abstract: Abstract The growing challenges posed by natural disasters highlight the pressing need for innovative support tools and approaches to disaster management. This study explores the application of geospatial information visualization tools and technologies-specifically three-dimensional modelling with photogrammetry, point cloud, machine learning, and artificial intelligence-to enhance urban search and rescue operations following earthquakes. Based on first-hand field experience during the 2023 Kahramanmaraş Earthquake, the study utilizes real-time data collected on-site, including images and videos from various devices, to generate detailed point clouds and three-dimensional models. The methodology integrates multiple approaches to create accurate and actionable spatial representations of affected areas. Personal observations from the field highlighted limitations such as the impact of lighting, weather conditions, computing power, long processing times, and psychological stress on the rescuers. The key findings demonstrate the significant potential of these technologies in improving rescue operations by generating detailed models that can identify survivors, structural weaknesses, and safe entry points for rescuers. These tools enable the mapping of large disaster areas, facilitating coordinated efforts between local and international teams. Furthermore, these tools offer immersive training opportunities and can enhance artificial intelligence models for future disaster response by leveraging real disaster data to train models in recognizing survivors, assessing damage, and analysing terrain variations.

Keywords: Geospatial information visualization; USAR operations; Photogrammetry; Total station; Point cloud; ML; AI; Mapping; LiDAR; Kahramanmaras Earthquake (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11069-025-07712-3

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