arXiv AI By Emadeldeen Hamdan, Ahmad Faiz Tharima, Mohd Zahirasri Mohd Tohir, Dayang Nur Sakinah Musa, Erdem Koyuncu, Adam J. Watts, Ahmet Enis Cetin

Toward Generalizable Deep Learning Based Peatland Fire Detection via Walsh Hadamard Transform and Domain Adaptation

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arXiv Machine Learning
Sep 21

Multiclass Semantic Segmentation of Wildland Fire Images Using Context-Aware Centralized Copy-Paste Data Augmentation

arXiv:2609.21241v1 Announce Type: cross Abstract: Producing accurate annotations for deep learning based image segmentation is both costly and labor intensive. This challenge is especially evident in...

By Joon Tai Kim, Nishanth Kunchala, Vishv Patel, Tianle Chen, Ziyu Dong, Daniel Ospina Acero, Roger Williams, Mrinal Kumar
arXiv AI
Sep 17

Interpretable Patch-Based Deep Learning for Wildfire Spread Prediction from Ensemble Simulations

The study evaluates deep learning surrogates for wildfire spread prediction, training four architectures on 10,584 high‑resolution simulations from Catalonia. Results show that only surface fuel load significantly predicts burn probability, and convolutional models mainly use distance to the fire front while a transformer model emphasizes fuel and terrain. When applied to a new region without retraining, the models still perform reasonably, with only a modest accuracy drop.

By Marcin Lawenda, Aleksandra Krasicka, David Caballero, Luis Torres, {\L}ukasz Szustak
arXiv AI
Jul 7

Towards Generalizable Deepfake Image Detection with Vision Transformers

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.

By Kaliki V Srinanda, M Manvith Prabhu, Hemanth K Mogilipalem, Jayavarapu S Abhinai, Vaibhav Santhosh, Aryan Herur, Deepu Vijayasenan
arXiv Computer Vision
Sep 2

Multimodal RGB-Infrared Combination for UAV-Based Wildfire Segmentation: A Comparative Study on FLAME3

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.

By Matheus F. Kovaleski, Lu\'is Garrote, Cristiano Premebida, J\'er\^ome Mendes, Jo\~ao Ruivo Paulo