# GeoResponder: Towards Building Geospatial LLMs for Time-Critical Disaster Response

arXiv: https://arxiv.org/abs/2509.19354 · HTML: https://mlsanity.com/paper/2509.19354/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 26 references that are in the map.
- First posted on arXiv 18 Sep 2025.

## Builds on (most cited first, 16 of 16)

- [Language Models Represent Space and Time](https://mlsanity.com/paper/2310.02207/) (2023)
- [K2: A Foundation Language Model for Geoscience Knowledge Understanding and Utilization](https://mlsanity.com/paper/2306.05064/) (2023)
- [GPT4GEO: How a Language Model Sees the World's Geography](https://mlsanity.com/paper/2306.00020/) (2023)
- [GeoLLM: Extracting Geospatial Knowledge from Large Language Models](https://mlsanity.com/paper/2310.06213/) (2023)
- [GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding](https://mlsanity.com/paper/2310.14478/) (2023)
- [Are Large Language Models Geospatially Knowledgeable?](https://mlsanity.com/paper/2310.13002/) (2023)
- [CityGPT: Empowering Urban Spatial Cognition of Large Language Models](https://mlsanity.com/paper/2406.13948/) (2024)
- [ERNIE-GeoL: A Geography-and-Language Pre-trained Model and its Applications in Baidu Maps](https://mlsanity.com/paper/2203.09127/) (2022)
- [SpaBERT: A Pretrained Language Model from Geographic Data for Geo-Entity Representation](https://mlsanity.com/paper/2210.12213/) (2022)
- [Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations](https://mlsanity.com/paper/2505.17136/) (2025)
- [City Foundation Models for Learning General Purpose Representations from OpenStreetMap](https://mlsanity.com/paper/2310.00583/) (2023)
- [UrbanLLM: Autonomous Urban Activity Planning and Management with Large Language Models](https://mlsanity.com/paper/2406.12360/) (2024)
- [Evaluating the Ability of Large Language Models to Reason about Cardinal Directions](https://mlsanity.com/paper/2406.16528/) (2024)
- [LAMP: A Language Model on the Map](https://mlsanity.com/paper/2403.09059/) (2024)
- [Chatmap : Large Language Model Interaction with Cartographic Data](https://mlsanity.com/paper/2310.01429/) (2023)
- [Detecting Actionable Requests and Offers on Social Media During Crises Using LLMs](https://mlsanity.com/paper/2504.16144/) (2025)

## Built on by (most cited first, 1 of 1)

- [Empowering LLM Agents with Geospatial Awareness: Toward Grounded Reasoning for Wildfire Response](https://mlsanity.com/paper/2510.12061/) (2025)

## Abstract (arXiv)

LLMs excel at linguistic tasks but lack the inner geospatial capabilities needed for time-critical disaster response, where reasoning about road networks, coordinates, and access to essential infrastructure such as hospitals, shelters, and pharmacies is vital. We introduce GeoResponder, a framework that instills robust spatial reasoning through a scaffolded instruction-tuning curriculum. By stratifying geospatial learning into different cognitive layers, we anchor semantic knowledge to the continuous coordinate manifold and enforce the internalization of spatial axioms. Extensive evaluations across four topologically distinct cities and diverse tasks demonstrate that GeoResponder significantly outperforms both state-of-the-art foundation models and domain-specific baselines. These results suggest that LLMs can begin to internalize and generalize geospatial structures, pointing toward the future development of language models capable of supporting disaster response needs.