RoadMind: Towards a Geospatial AI Expert for Disaster Response
This work addresses the challenge of spatial understanding in AI for disaster response, enabling more effective offline systems, though it is incremental in applying existing methods to a new domain.
The authors tackled the problem of limited geospatial reasoning in LLMs for disaster response by developing RoadMind, a self-supervised framework that uses OpenStreetMap data to enhance LLMs, resulting in significant performance improvements over strong baselines on tasks like road segment identification and distance estimation across three cities.
Large Language Models (LLMs) have shown impressive performance across a range of natural language tasks, but remain limited in their ability to reason about geospatial data, particularly road networks, distances, and directions. This gap poses challenges in disaster scenarios, where spatial understanding is critical for tasks such as evacuation planning and resource allocation. In this work, we present RoadMind, a self-supervised framework that enhances the geospatial reasoning capabilities of LLMs using structured data from OpenStreetMap (OSM). Our automated pipeline extracts road infrastructure data for a given city and converts it into multiple supervision formats tailored to key spatial tasks. We pretrain and fine-tune LLMs on these representations using QLoRA adapters and 4-bit quantized models. We evaluate our approach on three disaster-prone cities with varying global representation, Los Angeles, Christchurch, and Manila, across tasks such as road segment identification, nearest road retrieval, and distance/direction estimation. Our results show that models trained via RoadMind significantly outperform strong baselines, including state-of-the-art LLMs equipped with advanced prompt engineering. This demonstrates the potential of structured geospatial data to enhance language models with robust spatial reasoning, enabling more effective offline AI systems for disaster response.