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. The code and dataset are publicly available on GitHub at https://github.com/Ayanzadeh93/_WildfireVLM_.

翻译：野火对生态系统、人类生命和基础设施构成日益严重的威胁，气候变化和人类活动导致其发生频率和强度不断上升。早期检测至关重要，但由于微弱烟雾信号、动态气象条件以及大范围实时分析的需求，基于卫星的监测仍面临挑战。我们提出WildfireVLM，这是一个将卫星影像野火检测与语言驱动风险评估相结合的AI框架。我们利用Landsat-8/9、GOES-16及其他公开地球观测来源的影像（包括对齐光谱波段的协同产品），构建了带标注的野火与烟雾数据集。WildfireVLM采用YOLOv12来检测火区和烟羽，充分借助其在卫星影像中识别微小复杂模式的能力。我们集成多模态大语言模型（MLLMs），将检测结果转化为情境化风险评估和优先级响应建议，以支持灾害管理。通过采用共享评分标准的LLM-as-judge评估方法，验证了风险推理的质量。该系统采用面向服务的架构部署，支持实时处理、可视化风险仪表盘和长期野火追踪，充分展现了计算机视觉与语言推理相结合对可扩展野火监测的价值。代码和数据集已通过GitHub公开获取：https://github.com/Ayanzadeh93/_WildfireVLM_。