Toward AI-driven fire imagery: Attributes, challenges, comparisons, and the promise of VLMs and LLMs

Despite recent advancements in technology-driven fire management systems, environment continues to grapple with the increasing frequency and severity of wildfires, an issue exacerbated by climate change. A recent example is the devastating California wildfire in January 2025, which burned approximately 182,197 acres, destroyed 16,306 structures, and resulted in a total economic loss of over 275 billion dollars. Such events underscore the urgent need for further investment in intelligent, data-driven fire management solutions. One transformative development in this field has been the deployment of Unmanned Aerial Systems (UAS) for wildfire monitoring and control. These systems capture multimodal imagery and sensor data, facilitating the development of advanced Artificial Intelligence (AI) models for fire detection, spread modeling and prediction, effective suppression, and post-incident damage assessment. Unfortunately, most existing wildfire datasets exhibit significant heterogeneity in terms of imaging modalities (e.g., RGB, thermal, IR), annotation quality, target applications, and geospatial attributes. This diversity often complicates the identification of appropriate datasets for new and emerging wildfire scenarios, which remains a core challenge that hampers progress in the field and limits generalizability and reusability. This paper presents a comprehensive review of prominent wildfire datasets, offering a systematic comparison across various dimensions to help researchers, especially newcomers, select the most suitable datasets for their needs. Additionally, it identifies key parameters to consider when designing and collecting new fire imagery datasets to enhance future usability. Another key contribution of this work is its exploration of how emerging Large Language Models/Vision Language Models (LLMs/VLMs) can catalyze the creation, augmentation, and application of wildfire datasets. We discuss the potential of these models to integrate global knowledge for more accurate fire detection, devise evacuation plans, and support data-driven fire control strategies.