# Empowering LLM Agents with Geospatial Awareness: Toward Grounded Reasoning for Wildfire Response

arXiv: https://arxiv.org/abs/2510.12061 · HTML: https://mlsanity.com/paper/2510.12061/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning.
- Builds on 7 of its 50 references that are in the map.
- First posted on arXiv 14 Oct 2025.

## Placed in

- LLM reasoning / [Spatial and embodied reasoning](https://mlsanity.com/lr/spatial-and-embodied-reasoning/)
- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 7 of 7)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [DisasterResponseGPT: Large Language Models for Accelerated Plan of Action Development in Disaster Response Scenarios](https://mlsanity.com/paper/2306.17271/) (2023)
- [WildfireGPT: Tailored Large Language Model for Wildfire Analysis](https://mlsanity.com/paper/2402.07877/) (2024)
- [From Pixels to Places: A Systematic Benchmark for Evaluating Image Geolocalization Ability in Large Language Models](https://mlsanity.com/paper/2508.01608/) (2025)
- [RAG for Geoscience: What We Expect, Gaps and Opportunities](https://mlsanity.com/paper/2508.11246/) (2025)
- [Predicting Human Mobility during Extreme Events via LLM-Enhanced Cross-City Learning](https://mlsanity.com/paper/2507.19737/) (2025)
- [GeoResponder: Towards Building Geospatial LLMs for Time-Critical Disaster Response](https://mlsanity.com/paper/2509.19354/) (2025)

## Built on by (most cited first, 9 of 9)

- [Agentic AI in Remote Sensing: Foundations, Taxonomy, and Emerging Systems](https://mlsanity.com/paper/2601.01891/) (2026)
- [Physically Interpretable AlphaEarth Foundation Model Embeddings Enable LLM-Based Land Surface Intelligence](https://mlsanity.com/paper/2602.10354/) (2026)
- [Characterizing AlphaEarth Embedding Geometry for Agentic Environmental Reasoning](https://mlsanity.com/paper/2604.18715/) (2026)
- [CrisiSense-RAG: Crisis Sensing Multimodal Retrieval-Augmented Generation for Rapid Disaster Impact Assessment](https://mlsanity.com/paper/2602.13239/) (2026)
- [Learning Agent Routing From Early Experience](https://mlsanity.com/paper/2605.07180/) (2026)
- [GeoDisaster: Benchmarking Orchestrated Agents for Operational Disaster Geo-Intelligence](https://mlsanity.com/paper/2606.17246/) (2026)
- [Risk Is Not the Target: A Monotonic Framework for Evaluating Wildfire Operational Risk Signals](https://mlsanity.com/paper/2607.21597/) (2026)
- [Retrieval-Based In-Context Learning: A Domain Adaptation Framework](https://mlsanity.com/paper/2610.05717/) (2026)
- [AI4Fire: Evaluating Large Language Models on Wildfire Tasks](https://mlsanity.com/paper/2610.10946/) (2026)

## Abstract (arXiv)

Effective disaster response is essential for safeguarding lives and property. Existing statistical approaches often lack semantic context, generalize poorly across events, and offer limited interpretability. While Large language models (LLMs) provide few-shot generalization, they remain text-bound and blind to geography. To bridge this gap, we introduce a Geospatial Awareness Layer (GAL) that grounds LLM agents in structured earth data. Starting from raw wildfire detections, GAL automatically retrieves and integrates infrastructure, demographic, terrain, and weather information from external geodatabases, assembling them into a concise, unit-annotated perception script. This enriched context enables agents to produce evidence-based resource-allocation recommendations (e.g., personnel assignments, budget allocations), further reinforced by historical analogs and daily change signals for incremental updates. We evaluate the framework in real wildfire scenarios across multiple LLM models, showing that geospatially grounded agents can outperform baselines. The proposed framework can generalize to other hazards such as floods and hurricanes.