Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

WildfireVLM: AI-powered Analysis for Early Wildfire Detection and Risk Assessment Using Satellite Imagery

Aydin Ayanzadeh, Prakhar Dixit, Sadia Kamal, Milton Halem

TL;DR

The WildfireVLM system is deployed using a service-oriented architecture that supports real-time processing, visual risk dashboards, and long-term wildfire tracking, demonstrating the value of combining computer vision with language-based reasoning for scalable wildfire monitoring.

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 recommendations for disaster management. We validate the quality of risk reasoning using an LLM-as-judge evaluation with a shared rubric. The system is deployed using a service-oriented architecture that supports real-time processing, visual risk dashboards, and long-term wildfire tracking, demonstrating the value of combining computer vision with language-based reasoning for scalable wildfire monitoring.

WildfireVLM: AI-powered Analysis for Early Wildfire Detection and Risk Assessment Using Satellite Imagery

TL;DR

The WildfireVLM system is deployed using a service-oriented architecture that supports real-time processing, visual risk dashboards, and long-term wildfire tracking, demonstrating the value of combining computer vision with language-based reasoning for scalable wildfire monitoring.

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 recommendations for disaster management. We validate the quality of risk reasoning using an LLM-as-judge evaluation with a shared rubric. The system is deployed using a service-oriented architecture that supports real-time processing, visual risk dashboards, and long-term wildfire tracking, demonstrating the value of combining computer vision with language-based reasoning for scalable wildfire monitoring.
Paper Structure (11 sections, 3 figures, 1 table)

This paper contains 11 sections, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Data Collection
  4. System Architecture
  5. Object Detection
  6. Language Model
  7. Experimental Results
  8. Implementation Details
  9. Object Detection
  10. Language Model Evaluation
  11. Conclusion

Figures (3)

  • Figure 1: Demonstration of GOES-16 and Landsat 8 dataset.
  • Figure 2: System architecture of WildfireVLM, consisting of four integrated modules: (1) Input module processes satellite imagery from Landsat-8/9 through data preprocessing; (2) YOLOv12 Detection Core performs YOLO-based object detection to identify fire zones and smoke plumes, outputting bounding boxes, confidence scores, and object classes; (3) Vision-Language Risk Reasoning module employs language model for contextual understanding, generating risk assessments, analytical insights, and actionable recommendations; and (4) Risk and Output module stores detection results in a historical fire database, enables temporal pattern analysis, and generates comprehensive risk reports with visual summaries for decision support.
  • Figure 3: Wildfire risk assessment comparison between GPT-4o and Claude 3.5 Sonnet on the proposed dataset. Left: Demonstration of wildfire detection. Right: LLM-generated structured risk assessments including general observations, fire behavior analysis, spread potential, severity classification, and actionable recommendations/ Insight Action.