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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. 

Wildfires propagate through interactions between wind, fuel, and terrain, resulting in complex behaviors that challenge accurate predictions. This study investigates the interaction between wind velocity topology and wildfire dynamics, aiming to enhance our understanding of wildfire spread patterns through a simplified nonlinear convection–diffusion–reaction wildfire model, adopting a fundamental reactive flow dynamics perspective. We revisited the non-dimensionalizion of the governing combustion model by incorporating three distinct time scales. This approach revealed two new non-dimensional numbers, contrasting with the conventional non-dimensionalization that considers only a single time scale. Through scaling analysis, we analytically identified the critical determinants of transient wildfire behavior and established a state-neutral curve, indicating where initial wildfires extinguish for specific combinations of the identified non-dimensional numbers. Subsequently, a wildfire transport solver was developed, integrating upwind compact schemes and implicit–explicit Runge–Kutta methods. We explored the influence of stable and unstable manifolds in wind topology on the transport of wildfire under steady wind conditions defined using a saddle-type fixed point flow, emphasizing the role of the non-dimensional numbers. Additionally, we considered the benchmark unsteady double-gyre flow, examined the effect of unsteady wind topology on wildfire propagation, and quantified the wildfire response to varying wind oscillation frequencies and amplitudes using a transfer function approach. The results were compared to Lagrangian coherent structures (LCS) used to characterize the correspondence of manifolds with wildfire propagation. The approach of utilizing the wind flow manifolds provides valuable insight into wildfire dynamics across diverse wind scenarios, offering a potential tool for improved predictive modeling and management strategies.