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

Abstract

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.