![[2602.13305] WildfireVLM: AI-powered Analysis for Early Wildfire Detection and Risk Assessment Using Satellite Imagery](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.13305] WildfireVLM: AI-powered Analysis for Early Wildfire Detection and Risk Assessment Using Satellite Imagery
Summary
WildfireVLM introduces an AI framework for early wildfire detection and risk assessment using satellite imagery, enhancing disaster management capabilities.
Why It Matters
As wildfires become more frequent and intense due to climate change, effective early detection is crucial. WildfireVLM leverages advanced AI techniques to improve monitoring and response, potentially saving lives and resources. This research highlights the intersection of computer vision and language processing in addressing environmental challenges.
Key Takeaways
- WildfireVLM combines satellite imagery with AI for early wildfire detection.
- The framework utilizes YOLOv12 for detecting fire zones and smoke plumes.
- Multimodal Large Language Models provide contextual risk assessments.
- Real-time processing and visual dashboards enhance disaster management.
- The study validates its approach through an LLM-as-judge evaluation.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.13305 (cs) [Submitted on 9 Feb 2026] Title:WildfireVLM: AI-powered Analysis for Early Wildfire Detection and Risk Assessment Using Satellite Imagery Authors:Aydin Ayanzadeh, Prakhar Dixit, Sadia Kamal, Milton Halem View a PDF of the paper titled WildfireVLM: AI-powered Analysis for Early Wildfire Detection and Risk Assessment Using Satellite Imagery, by Aydin Ayanzadeh and 3 other authors View PDF HTML (experimental) Abstract:Wildfires are a growing threat to ecosystems, human lives, and infrastructure, with their frequency and intensity rising due to climate change and human activities. Early detection is critical, yet satellite-based monitoring remains challenging due to faint smoke signals, dynamic weather conditions, and the need for real-time analysis over large areas. We introduce WildfireVLM, an AI framework that combines satellite imagery wildfire detection with language-driven risk assessment. We construct a labeled wildfire and smoke dataset using imagery from Landsat-8/9, GOES-16, and other publicly available Earth observation sources, including harmonized products with aligned spectral bands. WildfireVLM employs YOLOv12 to detect fire zones and smoke plumes, leveraging its ability to detect small, complex patterns in satellite imagery. We integrate Multimodal Large Language Models (MLLMs) that convert detection outputs into contextualized risk assessments and prioritized response recommendati...