Remote Sensing Image Segmentation

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Remote sensing image segmentation assigns classes or object boundaries to pixels in satellite and aerial images, supporting tasks such as land-cover mapping, flood and wildfire monitoring, and building or road extraction. Common approaches include U-Net-style convolutional networks, transformers and state-space models, and fusion of optical, SAR, elevation, or time-series data; newer work also adapts geospatial foundation and vision-language models. Benchmarks such as ISPRS Vaihingen and Potsdam, LoveDA, OpenEarthMap, and Sen1Floods11 test accuracy across varied sensors, regions, and applications. A central challenge is reliable transfer under geographic, seasonal, and sensor shifts, while preserving fine boundaries and balancing annotation needs, computational cost, and performance on rare classes.

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220 papers21 papers in the last four weeks, up 17% on the four weeks before. 0.1% of all new papers.

Mapping and Classification of Trees Outside Forests using Deep Learning
Oct 29, 2025Moritz Lucas, Hamid Ebrahimy, Viacheslav Barkov +3Remote Sensing Image Segmentation Agricultural Remote Sensing
Osnabrück University, Institute for Computer Science, Remote Sensing Osnabrück, Germany · Leibniz Institute for Agricultural Engineering and Bioeconomy, System Process Engineering, Germany · Osnabrück University, Institute of Cognitive Science, Artificial Intelligence, Germany +1
Post-Disaster Affected Area Segmentation with a Vision Transformer (ViT)-based EVAP Model using Sentinel-2 and Formosat-5 Imagery
Jul 21, 2025Yi-Shan Chu, Hsuan-Cheng WeiRemote Sensing Image Understanding Weakly Supervised Image Segmentation
Department of Mathematical Science, National Chengchi University, Taipei, Taiwan · Division of Satellite Data Application, Taiwan Space Agency, Hsinchu, Taiwan · Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
Intra- and Cross-Scale Feature Interaction Network for Referring Remote Sensing Image Segmentation
Jun 4, 2025Zhigang Yang, Huiguang Yao, Linmao Tian +3Referring Image Segmentation Remote Sensing Image Segmentation
School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an 710072, P.R. China · School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, P.R. China · department of Software, Northwest Institute of Nuclear Technology, Xi’an 710072, P.R. China
The P $^3$ Dataset: Pixels, Points and Polygons for Multimodal Building Vectorization
May 21, 2025Raphael Sulzer, Liuyun Duan, Nicolas Girard +1Remote Sensing Image Segmentation Building Footprint Extraction
Université Côte d’Azur, INRIA, Sophia-Antipolis, France · LuxCarta Technology, Mouans-Sartoux, France
From Pixels to Images: A Structural Survey of Deep Learning Paradigms in Remote Sensing Image Semantic Segmentation
May 21, 2025Quanwei Liu, Tao Huang, Jiaqi Yang +1Remote Sensing Image Segmentation Multimodal Semantic Segmentation
College of Science and Engineering, James Cook University, Cairns QLD 4878, Australia · Center for AI and Data Science Innovation, James Cook University, Cairns QLD 4878, Australia · Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706 USA +1
Tackling fluffy clouds: robust agricultural field boundary delineation from Sentinel-1 and Sentinel-2 satellite image time series
Sep 20, 2024Foivos I. Diakogiannis, Zheng-Shu Zhou, Jeff Wang +133D ViTs Remote Sensing Image Segmentation
Data61, CSIRO, Kensington WA, Australia · A&F, CSIRO, Floreat WA, Australia · A&F, CSIRO, Melbourne, VIC 3030, Australia +4
A locally statistical active contour model for SAR image segmentation can be solved by denoising algorithms
Jan 10, 2024Guangming LiuImage Segmentation Remote Sensing Image Segmentation
Yantai Science and Technology Association
Active contours driven by local and global intensity fitting energy with application to SAR image segmentation and its fast solvers
Dec 19, 2023Guangming LiuImage Segmentation Remote Sensing Image Segmentation
Yantai Science and Technology Association
SAR image segmentation algorithms based on I-divergence-TV model
Dec 9, 2023Guangming LiuImage Segmentation Remote Sensing Image Segmentation
Yantai Science and Technology Association
A global optimization SAR image segmentation model can be easily transformed to a general ROF denoising model
Dec 8, 2023Guangming LiuImage Segmentation Convex Relaxation
Yantai Science and Technology Association

Oct 29, 2025J/K move · Enter open · S save

Mapping and Classification of Trees Outside Forests using Deep Learning

Moritz Lucas, Hamid Ebrahimy, Viacheslav Barkov, Ralf Pecenka+2

Osnabrück University, Institute for Computer Science, Remote Sensing Osnabrück, Germany · Leibniz Institute for Agricultural Engineering and Bioeconomy, System Process Engineering, Germany · Osnabrück University, Institute of Cognitive Science, Artificial Intelligence, Germany+1

Remote Sensing Image Segmentation Agricultural Remote Sensing Land Cover Classification Remote Sensing

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Trees Outside Forests (TOF) play an important role in agricultural landscapes by supporting biodiversity, sequestering carbon, and regulating microclimates. Yet, most studies have treated TOF as a single class or relied on rigid rule-based thresholds, limiting ecological interpretation and adaptability across regions. To address this, we evaluate deep learning for TOF classification using a newly generated dataset and high-resolution aerial imagery from four agricultural landscapes in Germany. Specifically, we compare convolutional neural networks (CNNs), vision transformers, and hybrid CNN-transformer models across six semantic segmentation architectures (ABCNet, LSKNet, FT-UNetFormer, DC-Swin, BANet, and U-Net) to map four categories of woody vegetation: Forest, Patch, Linear, and Tree, derived from previous studies and governmental products. Overall, the models achieved good classification accuracy across the four landscapes, with the FT-UNetFormer performing best (mean Intersection-over-Union 0.74; mean F1 score 0.84), underscoring the importance of spatial context understanding in TOF mapping and classification. Our results show good results for Forest and Linear class and reveal challenges particularly in classifying complex structures with high edge density, notably the Patch and Tree class. Our generalization experiments highlight the need for regionally diverse training data to ensure reliable large-scale mapping. The dataset and code are openly available at https://github.com/Moerizzy/TOFMapper