Subnational Geocoding of Global Disasters Using Large Language Models

• URL: http://arxiv.org/abs/2511.14788v1
• Date: Thu, 13 Nov 2025 17:04:18 GMT
• Title: Subnational Geocoding of Global Disasters Using Large Language Models
• Authors: Michele Ronco, Damien Delforge, Wiebke S. Jäger, Christina Corbane,
• Abstract summary: Subnational location data of disaster events are critical for risk assessment and disaster risk reduction.<n>Disaster databases report locations in unstructured textual form, with inconsistent granularity or spelling, that make it difficult to integrate with spatial datasets.<n>We present a fully automated LLM-assisted workflow that processes and cleans textual location information using GPT-4o.
• Score: 0.04374837991804084
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Subnational location data of disaster events are critical for risk assessment and disaster risk reduction. Disaster databases such as EM-DAT often report locations in unstructured textual form, with inconsistent granularity or spelling, that make it difficult to integrate with spatial datasets. We present a fully automated LLM-assisted workflow that processes and cleans textual location information using GPT-4o, and assigns geometries by cross-checking three independent geoinformation repositories: GADM, OpenStreetMap and Wikidata. Based on the agreement and availability of these sources, we assign a reliability score to each location while generating subnational geometries. Applied to the EM-DAT dataset from 2000 to 2024, the workflow geocodes 14,215 events across 17,948 unique locations. Unlike previous methods, our approach requires no manual intervention, covers all disaster types, enables cross-verification across multiple sources, and allows flexible remapping to preferred frameworks. Beyond the dataset, we demonstrate the potential of LLMs to extract and structure geographic information from unstructured text, offering a scalable and reliable method for related analyses.

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