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
The article introduces an open image dataset for active‑fire segmentation in satellite imagery, comprising 2,148 image‑mask pairs from 25 California wildfires captured between July 2020 and August 2026. Each 512×512 pixel, three‑channel image is a Sentinel‑2 Level‑2A composite of bands B12, B11, and B8A, with a fixed linear rendering applied uniformly. Masks distinguish background, short‑wave‑infrared rule‑based active fire, and invalid observations, and the dataset includes chip‑level metadata, an incident‑disjoint split, and a mask‑blind analyst review of 233 test chips.
By Shreyan Mitra, Mohammadreza Narimani, Parastoo Farajpoor
The paper proposes a data-driven protocol that uses multispectral Landsat‑8 imagery and connected‑component analysis to characterize fire‑region size distributions for active wildfire segmentation. Three segmentation architectures—U‑Net, DeepLabV3+, and SegFormer—are evaluated under different SWIR‑based spectral configurations, with U‑Net showing the strongest robustness and SWIR2 consistently delivering the best results. The study highlights the importance of both spectral band selection and architectural design for robust satellite‑based active wildfire mapping, especially when training on low fire‑pixel density images.
By Matheus F. Kovaleski, Cristiano Premebida, Jo\~ao Ruivo Paulo
The paper presents a multi‑campaign UAV thermal image dataset for inert ordnance screening, comprising 5,855 labeled image pairs collected in Tennessee across diverse terrains and seasons. The authors trained YOLOV11l and RT‑DETR‑R50 models on 33 m and 15 m altitude data, achieving automated candidate detection, and provided practical guidelines for future humanitarian mine action surveys. The dataset and models aim to aid screening and prioritization for follow‑up technical surveys or EOD assessment, not to replace clearance operations.
By Chad Melton, PhD., Annabelle Kelton
arXiv:2609.12521v1 Announce Type: new
Abstract: Unmanned aerial vehicle facade inspection can combine red, green, and blue (RGB) imagery with thermal measurements to screen surface and subsurface ano...
By Yuan Yang, Shulei Li, Haobo Liang
arXiv:2606. 13042v1 Announce Type: new Abstract: In intelligent video surveillance, cameras record image sequences during day and night.
By Vanessa Buhrmester, Ann-Kristin Grosselfinger, David Munch, Michael Arens