arXiv Computer Vision

Scale-based Approach for Active Wildfire Segmentation on Satellite Imagery

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.

arXiv Computer Vision

2d ago

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

llms computer-vision multimodal

arXiv Machine Learning

Jul 21

Spectral-Morphological Attention U-Net: An Efficient Network for Active Wildfire Detection

arXiv:2607. 16472v1 Announce Type: cross Abstract: Over the past decades, the frequency of global wildfires has been increasing steadily.

By Yugong Zeng, Jonathan Wu

computer-vision benchmarks

arXiv AI

Jun 30

FLAME 3 Dataset: Unleashing the Power of Radiometric Thermal UAV Imagery for Wildfire Management

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.

By Bryce Hopkins, Leo ONeill, Michael Marinaccio, Mobin Habibpour, Eric Rowell, Russell Parsons, Sarah Flanary, Irtija Nazim, Carl Seielstad, Fatemeh Afghah

computer-vision

arXiv AI

Jun 16

Near--Real-Time Conflict-Related Fire Detection in Sudan Using Unsupervised Deep Learning

arXiv:2512. 07925v4 Announce Type: replace-cross Abstract: Ongoing armed conflict in Sudan highlights the need for rapid monitoring of conflict-related fire-affected areas.

By Kuldip Singh Atwal, Dieter Pfoser, Daniel Rothbart

rag

arXiv Machine Learning

Aug 11

Data-Driven Fire-Zone Segmentation for Improved Short-Term Wildfire Prediction

arXiv:2608. 07472v1 Announce Type: new Abstract: Wildfire prediction models typically discretize study areas into uniform grids, ignoring the heterogeneous spatial distribution of ignitions.

By Nicolas Caron, Christophe Guyeux, Hassan Noura, Benjamin Aynes

computer-vision

arXiv AI

3d ago

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

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...

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

fine-tuning

arXiv AI

Jul 16

Post-Disaster Affected Area Segmentation with a Vision Transformer (ViT)-based EVAP Model using Sentinel-2 and Formosat-5 Imagery

arXiv:2507. 16849v3 Announce Type: replace-cross Abstract: We propose a vision transformer (ViT)-based deep learning framework to refine disaster-affected area segmentation from remote sensing imagery, aiming to support and enhance the Emergent Value Added Product (EVAP) developed by the Taiwan Space Agency (TASA).

By Yi-Shan Chu, Hsuan-Cheng Wei

llms computer-vision

arXiv Machine Learning

Jun 19

Integrating national forest inventory, airborne lidar, and satellite imagery for wall-to-wall mapping of forest structure with computer vision

arXiv:2606. 20291v1 Announce Type: new Abstract: Remote sensing is increasingly relied upon to deliver actionable science for forest and wildfire risk management across large landscapes.

By Luke J. Zachmann, David D. Diaz, Vincent A. Landau, Chelsey Walden-Schreiner, Tony Chang, Nathan E. Rutenbeck, Katharyn A. Duffy, Kiarie Ndegwa, Andreas Gros, Scott Conway, Guy Bayes

computer-vision

arXiv Machine Learning

Jul 28

When Less Is More: A Controlled Benchmark of Lightweight CNNs for Satellite Land-Cover Segmentation on DeepGlobe

arXiv:2607. 23024v1 Announce Type: cross Abstract: High-resolution satellite imagery is the backbone of good land-cover classification, and without that, environmental monitoring, urban planning, and sustainable resource management all fall short.

By Atiq Ur Rehman, Joseph Michael Donovan

computer-vision fine-tuning benchmarks

arXiv AI

Aug 19

Multi-Scale Spectral Attention Module-based Hyperspectral Segmentation in Autonomous Driving Scenarios

The paper investigates a Multi-Scale Spectral Attention Module (MSAM) for hyperspectral image segmentation in autonomous driving. MSAM uses three parallel 1D convolutions with different kernel sizes (1–11) and adaptive feature aggregation, integrated into UNet’s skip connections. Experiments on urban driving datasets show that MSAM improves mIoU by 2.32% and mF1 by 2.88% over baseline UNet-SC while keeping GPU performance competitive, with optimal kernel combinations varying by dataset.

By Imad Ali Shah, Jiarong Li, Tim Brophy, Martin Glavin, Edward Jones, Enda Ward, Brian Deegan

agents computer-vision robotics benchmarks

arXiv Computer Vision

Aug 28

Mapping Woody Vegetation from Multi-Source Imagery and Prediction Fusion for Enhanced Data Efficiency and Accuracy

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.

By Kal Backman, Jared Wood, Adam Roff

arXiv Machine Learning

Aug 14

Evaluating AlphaEarth Foundations Embeddings for Wildfire Susceptibility Mapping

arXiv:2608. 12663v1 Announce Type: cross Abstract: Wildfire susceptibility mapping typically relies on physical variables assembled from multiple remote-sensing, climate, and geospatial products.

By Yuan Zhuang, Sanaa Hobeichi, Peng Shi, Fei Huang

rag

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