arXiv:2607. 16472v1 Announce Type: cross Abstract: Over the past decades, the frequency of global wildfires has been increasing steadily.
arXiv:2606.10174v2 Announce Type: replace Abstract: Wildfire detection and monitoring are critical for mitigating fire spread and reducing environmental and infrastructural damage. In this work, we i...
arXiv:2610.01994v1 Announce Type: new Abstract: Wildfires pose severe risks to human life, ecosystems, and property. This study presents a machine learning approach for wildfire detection from GOES A...
The paper presents a lightweight vision‑language encoder paired with a compact multi‑layer perceptron to estimate flare combustion efficiency from low‑cost thermal video. The trained model is deployed via a user‑friendly GUI that overlays efficiency values on each frame, tracks real‑time trends, visualizes distribution across the video, and exports CSV reports. In a six‑month trial, the system maintained 99% uptime with under 15 minutes of weekly maintenance.
arXiv:2608. 07472v1 Announce Type: new Abstract: Wildfire prediction models typically discretize study areas into uniform grids, ignoring the heterogeneous spatial distribution of ignitions.
arXiv:2609.18227v1 Announce Type: new Abstract: Wildfire smoke detection from satellite imagery is critical for early warning and rapid response. For onboard satellite deployment, detection systems m...
arXiv:2606. 07648v1 Announce Type: cross Abstract: Air pollution represents one of the most critical environmental and public health challenges globally, with traditional sensor-based monitoring systems facing significant scalability and economic constraints.
arXiv:2412. 02831v2 Announce Type: replace-cross Abstract: The increasing accessibility of radiometric thermal imaging sensors for unmanned aerial vehicles (UAVs) offers significant potential for advancing AI-driven aerial wildfire management.
The paper examines RGB‑infrared fusion for binary wildfire segmentation using UAV imagery on the FLAME3 dataset. It compares RGB and infrared baselines with three fusion strategies across U‑Net, DeepLabV3+, and SegFormer architectures. Results show thermal data dominates segmentation performance, and feature‑level fusion with transformer‑based models yields the best results.
arXiv:2208. 00657v2 Announce Type: cross Abstract: Building detection and change detection using remote sensing images can help urban and rescue planning.
arXiv:2603. 21048v2 Announce Type: replace-cross Abstract: The identification of hazardous driving behaviors from in-cabin video streams is essential for enhancing road safety and supporting the detection of traffic violations and unsafe driver actions.
arXiv:2603.02465v2 Announce Type: replace-cross Abstract: Machine learning-based wildfire detection has advanced significantly using deep learning models trained on large wildfire image and video dat...