Toward Generalizable Deep Learning Based Peatland Fire Detection via Walsh Hadamard Transform and Domain Adaptation
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arXiv:2604. 17376v2 Announce Type: replace-cross Abstract: In today's day and age, we face a challenge in detecting deepfake images because of the fast evolution of modern generative models and the poor generalization capability of existing methods.
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.
The paper presents a framework that enhances deep‑learning tree‑cover mapping in New South Wales by fusing multiple imagery sources and normalizing image quality. It introduces an image‑composition technique that removes defects and a prediction‑fusion method that reduces reliance on any single image, together cutting errors by 38.2 % and 53.6 % respectively. Label transfer across diverse imagery further boosts data efficiency, yielding error reductions of 28.1 %–76.2 % and a 13‑fold decrease in performance variability across dates.
arXiv:2606. 17403v1 Announce Type: cross Abstract: Rapid assessment of building damage from satellite imagery is essential for effective disaster response and recovery.
arXiv:2608. 05265v1 Announce Type: new Abstract: Prediction of post-wildfire debris flows is critical for mitigating hazards to communities, infrastructure, and resources during intense rainfall in recently burned areas.
arXiv:2608. 03096v1 Announce Type: cross Abstract: Recent advances in video generation models have significantly intensified the deepfake threat, yet the current deepfake video detection benchmarks remain underdeveloped.