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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. 

Actor-critic methods, like Twin Delayed Deep Deterministic Policy Gradient (TD3), depend on basic noise-based exploration, which can result in less than optimal policy convergence. In this study, we introduce Monte Carlo Beam Search (MCBS), a new hybrid method that combines beam search and Monte Carlo rollouts with TD3 to improve exploration and action selection. MCBS produces several candidate actions around the policy's output and assesses them through short-horizon rollouts, enabling the agent to make better-informed choices. We test MCBS across various continuous-control benchmarks, including HalfCheetah-v4, Walker2d-v5, and Swimmer-v5, showing enhanced sample efficiency and performance compared to standard TD3 and other baseline methods like SAC, PPO, and A2C. Our findings emphasize MCBS's capability to enhance policy learning through structured look-ahead search while ensuring computational efficiency. Additionally, we offer a detailed analysis of crucial hyperparameters, such as beam width and rollout depth, and explore adaptive strategies to optimize MCBS for complex control tasks. Our method shows a higher convergence rate across different environments compared to TD3, SAC, PPO, and A2C. For instance, we achieved 90% of the maximum achievable reward within around 200 thousand timesteps compared to 400 thousand timesteps for the second-best method. 

Current forest monitoring technologies including satellite remote sensing, manned/piloted aircraft, and observation towers leave uncertainties about a wildfire’s extent, behavior, and conditions in the fire’s near environment, particularly during its early growth. Rapid mapping and real-time fire monitoring can inform in-time intervention or management solutions to maximize beneficial fire outcomes. Drone systems’ unique features of 3D mobility, low flight altitude, and fast and easy deployment make them a valuable tool for early detection and assessment of wildland fires, especially in remote forests that are not easily accessible by ground vehicles. In addition, the lack of abundant, well-annotated aerial datasets – in part due to unmanned aerial vehicles’ (UAVs’) flight restrictions during prescribed burns and wildfires – has limited research advances in reliable data-driven fire detection and modeling techniques. While existing wildland fire datasets often include either color or thermal fire images, here we present (1) a multi-modal UAV-collected dataset of dual-feed side-by-side videos including both RGB and thermal images of a prescribed fire in an open canopy pine forest in Northern Arizona and (2) a deep learning-based methodology for detecting fire and smoke pixels at accuracy much higher than the usual single-channel video feeds. The collected images are labeled to “fire” or “no-fire” frames by two human experts using side-by-side RGB and thermal images to determine the label. To provide context to the main dataset’s aerial imagery, the included supplementary dataset provides a georeferenced pre-burn point cloud, an RGB orthomosaic, weather information, a burn plan, and other burn information. By using and expanding on this guide dataset, research can develop new data-driven fire detection, fire segmentation, and fire modeling techniques.