Conference Agenda

Early Wildfire detection using AI remains challenging in environmental monitoring, particularly when the approach should be flexible enough to handle different sensors and different landscapes. This study presents a multi-stage deep learning framework for real-time smoke and fire detection using imagery from fixed tower cameras and UAVs. The proposed system employs YOLOv11 as the primary detection model for high-speed inference, com-plemented by Faster R-CNN for precision benchmarking and cross-architecture evaluation. Together, these models support an in-depth analysis of detection accuracy, robust-ness, and computational efficiency across diverse envi-ronmental conditions.

An end-to-end pipeline has been developed, integrating real-time image acquisition, asynchronous message han-dling through RabbitMQ, and performance logging via InfluxDB, enabling continuous model evaluation under near-operational conditions. Experimental results indicate that while YOLOv11 achieves high frame-rate perfor-mance and strong detection capability, it remains suscepti-ble to false positives in visually ambiguous scenarios such as haze, fog, or low-contrast backgrounds, where contex-tual patterns closely resemble smoke. Faster R-CNN serves as a complimentary reference to quantify localiza-tion accuracy and analyse error propagation, facilitating threshold tuning and model interpretability.

The presented framework bridges the gap between aca-demic model development and field-deployable fire sur-veillance systems. It establishes a reproducible, scalable foundation for real-time decision support in forest watch-tower networks and autonomous UAV missions aimed at early wildfire detection and response

Wildfires induce significant changes in forest structure and the surface reflectance characteristics. This study evaluated the effectiveness of using airborne LiDAR Intensity data to delineate surface fire burn areas in wildfires. We extracted ground returns from both coniferous and deciduous forests and conducted qualitative assessment of Intensity through Intensity images, as well as statistical evaluation using the non-parametric Mann–Whitney U test to compare burned and unburned areas. We compared the median and standard deviation of Intensity at a 10-m mesh scale, calculating standard deviation at a finer 0.5-m mesh resolution. The results revealed significant differences between the two groups. As a result, a significant difference was observed between the two groups. The effect size r for the median in deciduous forests ranged from 0.55 to 0.84, while the effect size r for the standard deviation in coniferous forests ranged from 0.32 to 0.47. Both indicated a medium to large effect. These findings suggest that LiDAR Intensity can effectively identify surface fire burn areas even under heterogeneous forest floor conditions. The proposed method has the potential to contribute to enhancing post-fire monitoring using airborne LiDAR.

This study investigates two decade (2000-2020) of Aboveground Biomass dynamics in the biodiversity hotspot of Western Ghats, India, focusing on the impacts on forest fire and climate variability. Using machine learning approaches with GEDI LiDAR data and MODIS satellite imagery, we developed a robust annual AGB model. These analysis reveals a consistent decline in AGB across Kodaikanal and Nilgiris. Results shows that rising temperature and vapor pressure deficit are the key driver for increase in burn are and fire intensity. These are pushing carbon rich evergreen forests toward a critical transition from carbon sink to source. An integrated Structural Equation Model confirms that the dominant role of climate in driving fire regimes and subsequent biomass loss. This research provides a critical scientific foundation for fire adaptive forest management and carbon accounting in vulnerable tropical ecosystem.

Wildfires are longstanding natural phenomena with significant impacts on ecosystems and communities. In recent years, Canada has experienced particularly severe wildfire effects, especially in British Columbia (BC), which has endured prolonged and impactful wildfire events. However, there is currently no specialized wildfire time-series dataset for BC that considers long-term temporal sequences and multiple driving factors, which are essential for data-driven approaches. To facilitate future research on data-driven wildfire risk and spread prediction, we have developed a dataset covering the entire BC province, encompassing 683 wildfire events from 2002-2023 at 500m resolution with daily observations. For each wildfire event, the dataset includes 20 driving factors, including vegetation status, meteorological factors, human activities, topographical features, and active fire detection. Based on this benchmark and similar datasets from other regions, we compared multiple deep learning models, including CNN-based, Transformer-based, and Mamba-based architectures, to explore the effectiveness of existing deep learning models in wildfire risk prediction. We found that model F1 scores were below 0.6, indicating that this new dataset presents a challenging non-linear modeling scenario that requires more advanced and tailor-designed deep learning models to improve wildfire risk prediction accuracy.

Wildfires are known as one of the most important natural hazards, adversely impacting the ecosystems and human lives. Monitoring and management of wildfires is necessary to minimize their negative effects. Global BA products are widely used to study wildfires, but their accuracy is not constant over different environments. In this study, the MCD64A1 BA product was spatially validated using ground truth maps in a fire-prone Zagros Forest over 2021-2023. Our results indicated that its performance varies temporally, as the Kappa coefficient ranged from 0.04 to 0.69. Overall accuracy was higher than 0.96 percent in all years, indicating that MCD64A1 can be considered as a source for studying wildfires; however, its underestimation should be considered. In the next step, the trend of fire and its relationship with precipitation (i.e., obtained from the CHRIPS dataset) were analyzed in three forest ecosystems from 2001 to 2024. In two regions, Marivan and Kermanshah, wildfires experienced an increasing trend, in contrast to the other region, Shiraz, where they decreased over time. Analyzing the correlation between fire and precipitation revealed that spring precipitation is more connected to BA than annual precipitation. Comparing the results of the three regions showed that this matter is also region-related, and the results of one region cannot be referred to another. This study provided information on the performance of MCD64A1 in semiarid forests and the wildfire conditions in the Zagros Mountains to aid wildfire management.