# Automated Wildfire Damage Assessment from Multi view Ground level Imagery Via Vision Language Models

arXiv: https://arxiv.org/abs/2509.01895 · HTML: https://mlsanity.com/paper/2509.01895/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 5 of its 30 references that are in the map.
- First posted on arXiv 2 Sep 2025.

## Placed in

- Multimodal LLMs / vision-language models / [Embodied and 3D vision-language models](https://mlsanity.com/mllm/embodied-and-3d-vision-language-models/)

## Builds on (most cited first, 5 of 5)

- [Remote Sensing Vision-Language Foundation Models without Annotations via Ground Remote Alignment](https://mlsanity.com/paper/2312.06960/) (2023)
- [Assessing Post-Disaster Damage from Satellite Imagery using Semi-Supervised Learning Techniques](https://mlsanity.com/paper/2011.14004/) (2020)
- [ELEV-VISION-SAM: Integrated Vision Language and Foundation Model for Automated Estimation of Building Lowest Floor Elevation](https://mlsanity.com/paper/2404.12606/) (2024)
- [Hyperlocal disaster damage assessment using bi-temporal street-view imagery and pre-trained vision models](https://mlsanity.com/paper/2504.09066/) (2025)
- [ELEV-VISION: Automated Lowest Floor Elevation Estimation from Segmenting Street View Images](https://mlsanity.com/paper/2306.03050/) (2023)

## Built on by (most cited first, 4 of 4)

- [Recov-Vision: Linking Street View Imagery and Vision-Language Models for Post-Disaster Recovery](https://mlsanity.com/paper/2509.20628/) (2025)
- [GraphFire-X: Physics-Informed Graph Attention Networks and Structural Gradient Boosting for Building-Scale Wildfire Preparedness at the Wildland-Urban Interface](https://mlsanity.com/paper/2512.20813/) (2025)
- [A Simulation-Grounded Agentic VLM Framework for Wildfire Monitoring and Reporting](https://mlsanity.com/paper/2610.02451/) (2026)
- [FireGraphNet: Sub-daily wildfire spread prediction with directed graph neural networks](https://mlsanity.com/paper/2610.19191/) (2026)

## Abstract (arXiv)

The escalating intensity and frequency of wildfires demand innovative computational methods for rapid and accurate property damage assessment. Traditional methods are often time-consuming, while modern computer vision approaches typically require extensive labeled datasets, hindering immediate post-disaster deployment. This research introduces a novel, zero-shot framework leveraging pre-trained multimodal large language models (MLLMs) to classify damage from ground-level imagery. Using Generative Pre-trained Transformer 4o (GPT-4o) as the primary model with comparative validation against Qwen2.5-Vision-Language-32-Billion-Instruct (Qwen), the research evaluates two pipelines applied to the 2025 Eaton and Palisades fires in California. These pipelines include an end-to-end inference method (Pipeline A) and a decoupled workflow where visual cues drive text-based classification (Pipeline B). A primary contribution of this study is demonstrating the efficacy of MLLMs in synthesizing information from multiple perspectives. The findings show that while single-view assessments struggle to classify intermediate damage, a multi-view analysis yields dramatic improvements. To explore the impact of prompting methods, the research benchmarked a baseline zero-shot and heuristic approach against advance reasoning strategies (Structured-Chain-of-Thought and Self-Consistency). The results indicate that simple prompting methods achieve a comparable accuracy to the reasoning strategies.