Awesome Remote Sensing Change Detection

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A curated list of datasets and code repositories focused on remote sensing change detection.

Contents

Datasets

Optical Datasets

Year Task Target Dataset Publication Source Image Pairs Image Size Resolution Location Class
2025 BCD Land cover JL1-CD arXiv2025 Jilin-1 5,000 512×512 0.5-0.75m Multiple provinces in China 2
2025 SCD Building EBD JRS2025 WorldView-3 >18,000 512×512 0.3-0.5m Global 7
2024 BCD Mine MineNetCD TGRS2024 Google Earth 71,711 256×256 1.2m Global 2
2024 BCD Building TUE-CD TGRS2024 WorldView-2 1,656 256×256 1.8m Turkey 2
2024 SCD Cropland CropSCD TGRS2024 - 4,141 512×512 0.5-2m Guangdong, China 9
2024 SCD Cropland Hi-CNA ISPRS P&RS 2024 GF-2 6,797 512×512 0.8m China (Hebei, Shanxi, Shandong, and Hubei) 5
2024 SCD Land cover ChangNet ICASSP2024 WayBack 31,000 1,900×1,200 0.3m 100 Cities in China 6
2023 BCD Building EGY-BCD GRSL2023 Google Earth 6,091 256×256 0.25m Egypt 2
2023 BCD Building HRCUS-CD TGRS2023 - 11,388 256×256 0.5m Zhuhai, China 2
2023 BCD Building SI-BU ISPRS P&RS 2023 Google Earth 4,932 512×512 0.2m Guiyang, China 2
2023 SCD Land cover CNAM-CD RS2023 Google Earth 2,503 512×512 0.5m 12 State-level New Areas in China 6
2023 SCD Land cover WUSU IJDE2023 GF-2 3 6,358×6,382 / 7,025×5,500 1m Wuhan, China 12
2023 BCD Landslide GVLM ISPRS P&RS 2023 Google Earth 17 1,748×1,748-10,808×7,424 0.59m Global 2
2023 SCD Building BANDON Sci. China Inf. Sci. 2023 Google Earth, Microsoft Virtual Earth, and ArcGIS 2,283 2,048×2,048 0.6m China (Beijing, Shanghai, Wuhan, Shenzhen, Hong Kong, and Jinan) 6
2023 SCD Land cover DynamicEarthNet CVPR2022 PlanetFusion 54,750 1,024×1,024 3m Global 7
2022 BCD Cropland CLCD JSTARS2022 GF-2 600 512×512 0.5-2m Guangdong, China 2
2022 RSICC Building LEVIR-CC TGRS2022 Google Earth 10,077 1,024×1,024 0.5m Texas, USA 2
2022 BCD Land cover SYSU-CD TGRS2021 - 20,000 256×256 0.5m Hong Kong, China 2
2022 SCD Building S2Looking RS2021 GF, SuperView, BJ-2 5,000 1,024×1,024 0.5-0.8m Global 2
2022 BCD Building LEVIR-CD+ RS2021 Google Earth 985 1,024×1,024 0.5m Texas, USA 2
2022 SCD Land cover Landsat-SCD IJDE2022 Landsat 8,468 416×416 30m Xinjiang, China 10
2022 SCD Building NanjingDataset ISPRS P&RS 2022 Google Earth 2,519 256×256 0.3m Nanjing, China 3
2022 RSICC Urban Dubai-CC TGRS2022 Landsat 7 500 50×50 30m Dubai 6
2022 SCD Flood SpaceNet 8 CVPR2022W Maxar 12 1,300×1,300 0.3-0.8m Germany, and Louisiana 4
2021 SCD Land cover MSD JSTARS2022 NAIP, Landsat-8, and NLCD 2,250 - 1m, 30m Maryland, USA 16
2021 SCD Land cover S2MTCP ICPR2021 Sentinel-2 1,520 600×600 10m Global -
2021 BCD Urban HTCD RS2021 Google Earth, Open Aerial Map 3,772 256×256, 2,048×2,048 0.5971m, 0.07465m Chisinau, Moldova 2
2020 BCD Building CD_Data_GZ (or GZ-CD) TGRS2020 Google Earth 19 1,006×1,168-4,936×5,224 0.55m Guangzhou, China 2
2020 BCD Building DSIFN (or DSIFN-CD) ISPRS P&RS 2020 Google Earth 3,940 512×512 - China (Beijing, Chengdu, Shenzhen, Chongqing, Wuhan, and Xian) 2
2020 BCD Building LEVIR-CD RS2020 Google Earth 637 1,024×1,024 0.5m Texas, USA 2
2020 SCD Land cover Hi-UCD arXiv2020 Aerial Images 1,293 1,024×1,024 0.1m Tallinn, Estonia 9
2020 SCD Land cover SECOND TGRS2021 Aerial Images 4,662 512×512 - China (Hangzhou, Chengdu, and Shanghai) 6
2020 BCD Building MUDS (or SpaceNet 7) CVPR2021 - - 1,024×1,024 4m Global 2
2019 BDA Building xBD arXiv2019 Maxar 11,034 1,024×1,024 <0.8m Global 4
2019 SCD Land cover HRSCD CVIU2019 IGN 291 10,000×10,000 0.5m France (Rennes, and Caen) 5
2018 BCD Building WHU-CD TGRS2018 Aerial Image 1 32,507×15,354 0.2m Christchurch, New Zealand 2
2018 BCD Building SVCD (or CDD) Int. Arch. Photogramm. Remote Sens. Spatial Inf. 2018 Google Earth 1,6000 256×256 0.03-1m - 2
2018 BCD Riverway The River Data Set TGRS2018 EO-1 Hyperion 1 463×241 30m Jiangsu, China 2
2018 BCD Land cover OSCD IGARSS2018 Sentinel-2 24 600×600 10-60m Global 2
2008 BCD Land cover SZTAKI TGRS2009 Aerial Images 13 952x640 1.5m -

Multi-Modal and SAR Datasets

Year Task Target Dataset Publication Source Image Pairs Image Size Resolution Location Class
2025 SCD Building BRIGHT arXiv2025 Optical and SAR 4,538 1,024×1,024 0.3-1m Global 4
2024 SCD Building Hi-BCD Information Fusion 2023 Aerial Images, DSMs 1,500 1,000×1,000 0.25m Netherlands (Amsterdam, Rotterdam, and Utrecht) 3
2024 SCD Flood UrbanSARFloods CVPRW2024 Sentinel-1 8,879 512×512 20m Global 5
2024 SCD Land use EVLab-CMCD ISPSR P&RS 2024 GF-2, BJ-2, Historical land use maps 5,622 512×512 0.8m 10 Cities in China 13
2023 BCD Flood CAU-Flood JAG2023 Sentinel-1, Sentinel-2 18,302 256×256 10m Global 2
2023 SCD Flood Kuro Siwo NeurIPS2024 Sentinel-1, DEM 67,490 224×224 10m Gloabl 3
2023 SCD Urban SMARS ISPRS P&RS 2023 Simulated Orthoimages and DSMs - 512×512 0.3m, 0.5m Simulated Paris and Venice 3
2023 BCD Urban 3DCD ISPRS P&RS 2023 Aerial Images, DSMs 472 400×400, 200×200 0.5m, 1m Valladolid, Spain 2
2023 SCD Urban Urb3DCD–V2 ISPRS P&RS 2023 ALS, Multi-Sensor - - - Simulated 7
2022 BCD Flood Wuhan JAG2022 Sentinel-2, COSMO-SkyMed 1 11,216×13,693 3m Wuhan, China 2
2022 BCD Flood Ombria JSTARS2022 Sentinel-1, Sentinel-2 1,688 256×256 10m Global 2
2021 BCD Land cover MultiModalOSCD ISPRS. XXIV ISPRS Congress 2021 Sentinel-1, Sentinel-2 24 600×600 10-60m Global 2

Contests

Year Target Contest Track Image Pairs Image Size Resolution Other
2024 Land cover ISPRS第一技术委员会多模态遥感应用算法智能解译大赛 基于高分辨率可见光图像的感兴趣区域内部变化智能检测 4,000 512×512 2m -
2024 Land cover “吉林一号”杯卫星遥感应用青年创新创业大赛 高分辨率遥感影像全要素变化检测研究 5,000 512×512 <0.75m -
2023 Cropland “吉林一号”杯卫星遥感应用青年创新创业大赛 基于高分辨率卫星影像的耕地变化检测 8,000 256×256 <0.75m -
2023 Land cover “国丰东方慧眼杯”遥感影像智能处理算法大赛 对象级变化检测 >6,000 512×512 1-2m -
2022 Land cover “航天宏图杯”遥感影像智能处理算法大赛 遥感影像变化检测 >6,000 512×512 1-2m -
2022 Flood SpaceNet8: Flood Detection Challenge Flood Detection Challenge Using Multiclass Segmentation 12 1,300×1,300 0.3-0.8m Dataset Paper, Solution Paper
2021 Land cover IEEE GRSS Data Fusion Contest Multitemporal Semantic Change Detection 2,250 - - Outcome Paper
2021 Land cover DynamicEarthNet Challenge Weakly-Supervised Unsupervised Binary Land Cover Change Detection, Multi-Class Change Detection 54,750 1,024x1,024 3.0 Top1 Solution, Dataset Paper
2021 Land cover “昇腾杯”遥感影像智能处理算法大赛 耕地建筑物变化检测 >6,000 512×512 1-2m Top4 Solution, Top5 Solution
2021 Building 遥感图像智能解译技术挑战赛 遥感图像建筑物变化检测 10,000 512×512 - -
2021 Building 慧眼“天智杯”人工智能挑战赛 可见光建筑智能变化检测 5,000 1,024×1,024 0.5-0.7m -
2020 Land cover 商汤科技首届AI遥感解译大赛 变化检测 4,662 512×512 0.5-3m Top1 Solution
2020 Land cover SpaceNet 7: Multi-Temporal Urban Development Challenge Multi-Temporal Urban Development Challenge - 1,024×1,024 4m Solutions, Dataset Paper
2019 Building xView2 Challenge (or xBD) Building Damage Assessment 11,034 1,024×1,024 - Dataset Paper

Open Source Toolbox

Year Abbreviation Description Other
2024 rschange An open-source toolbox dedicated to reproducing and developing advanced methods (e.g., DDLNet, CDMask) for change detection in remote sensing images. Last CommitGitHub stars
2024 torchange A benchmark library providing out-of-box, straightforward implementations of contemporary spatiotemporal change detection models (e.g., ChangeStar, Changen, AnyChange), metrics, and datasets to promote reproducibility in remote sensing research. Last CommitGitHub stars
2022 Open-CD The most comprehensive open-source toolbox for change detection, offering a unified platform with diverse methods, training/inference tools, data analysis scripts, and benchmarks to support research and development in the field. Paper: arXiv2024. Last CommitGitHub stars
2022 PaddleRS A remote sensing toolkit based on PaddlePaddle that supports change detection among other tasks, providing dedicated models (e.g., BIT, FarSeg), large-image processing capabilities, and practical tutorials for analyzing temporal land cover differences. The PyTorch version is called CDLab. Last CommitGitHub stars
2020 Change Detection Repository It provides Python implementations of selected traditional change detection methods (e.g., CVA, SFA, MAD) and deep learning-based approaches (e.g., SiamCRNN, DSFA, and FCN-based methods). Last CommitGitHub stars

Review Papers

Year Title Publication Description
2025 深度学习遥感变化检测研究进展：像素-对象-场景 遥感技术与应用2025 本文从像素级、对象级和场景级三个层次系统总结深度学习在遥感变化检测中的研究进展，结合典型案例分析其实际应用，并展望其未来发展趋势。
2025 On the use of Graphs for Satellite Image Time Series arXiv2025 Explores the integration of graph-based techniques for spatio-temporal analysis of satellite image time series, focusing on the construction of spatio-temporal graphs and their applications in tasks such as land cover mapping and water resource forecasting, along with future research perspectives.
2025 A Survey of Sample-Efficient Deep Learning for Change Detection in Remote Sensing: Tasks, Strategies, and Challenges GRSM2025 Summarizes literature on deep learning-based change detection methods for different tasks and strategies in sample-limited scenarios, discussing recent advances in image generation, self-supervision, and visual foundation models to address data scarcity.
2025 Deep learning change detection techniques for optical remote sensing imagery: Status, perspectives and challenges JAG2025 Systematically summarizes datasets, theories, and methods of change detection for optical remote sensing imagery, analyzing AI-based algorithms from the perspective of algorithm granularity and discussing challenges and trends in the AI era. Updates are available at daifeng2016/Awesome-Optical-Remote-Sensing-Datasets-and-Methods.
2024 Deep-Learning for Change Detection Using Multi-Modal Fusion of Remote Sensing Images: A Review RS2024 Explores the application of deep learning for change detection in remote sensing imagery using heterogeneous data sources, including multispectral, hyperspectral, radar, and multitemporal imagery, and discusses public datasets, models, challenges, and future trends.
2024 Deep Learning for Satellite Image Time-Series Analysis: A review GRSM2024 Summarizes state-of-the-art methods for modeling environmental and agricultural variables from satellite image time series (SITS) using deep learning, addressing the complexity of SITS data and its applications in land and natural resource management.
2024 Advances and Challenges in Deep Learning-Based Change Detection for Remote Sensing Images: A Review through Various Learning Paradigms RS2024 Comprehensively examines deep learning-based change detection in remote sensing, covering key architectures, learning paradigms (supervised, semi-supervised, weakly supervised, and unsupervised), benchmark datasets, and emerging opportunities such as self-supervised learning, foundation models, and multimodal data fusion, while highlighting current challenges and promising future research directions to advance the field.
2024 Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review RS2024 Presents a comprehensive survey of deep learning-based change detection in remote sensing over the past decade, offering a systematic taxonomy from perspectives of algorithm granularity, supervision modes, and frameworks, while reviewing key datasets, evaluation metrics, state-of-the-art performance, and identifying promising future research directions to guide and inspire the community.
2023 深度学习的遥感变化检测综述：文献计量与分析 遥感学报2023 本文综述了基于深度学习的遥感变化检测研究进展，从像素、对象和场景三个粒度系统梳理方法体系，指出对象与场景级方法更具优势，并强调未来需突破多模态异质数据融合、非理想样本处理及多元变化信息提取等挑战，以推动其在多领域更广泛、智能化的应用。
2023 人工智能时代的遥感变化检测技术：继承、发展与挑战 遥感学报2023 本文系统梳理了人工智能时代下光学遥感影像变化检测技术从传统方法向数据—模型—知识联合驱动的智能化转型历程，分析了无监督、监督与弱监督三类方法的发展趋势，并指出未来需重点突破模型可解释性、泛化迁移能力及跨场景跨领域应用等关键瓶颈问题。相关讲解视频详见：【前沿进展】变化检测与深度学习。
2023 3D urban object change detection from aerial and terrestrial point clouds: A review JAG2023 Reviews developments in 3D change detection for urban objects using point cloud data, analyzing buildings, street scenes, urban trees, and construction sites, and discusses data sources, methods, and future challenges.
2023 Change detection of urban objects using 3D point clouds: A review ISPRS P&RS 2023 Provides a comprehensive review of point-cloud-based 3D change detection for urban objects, covering data registration, variance estimation, change analysis, and applications in land cover monitoring, vegetation surveys, and construction automation.
2022 Land Cover Change Detection Techniques: Very-high-resolution optical images: A review GRSM2022 Reviews land cover change detection techniques using very-high-resolution remote sensing images, focusing on the ability to capture detailed changes and discussing various methods and applications.
2022 A Survey on Deep Learning-Based Change Detection from High-Resolution Remote Sensing Images RS2022 Reviews deep learning-based change detection methods for high-resolution remote sensing images, categorizing algorithms by network architecture, and discusses datasets, evaluation metrics, challenges, and future research directions.
2022 A review of multi-class change detection for satellite remote sensing imagery GSIS2022 Provides a comprehensive review of Multi-class Change Detection (MCD) in remote sensing, covering its background, key challenges, benchmark datasets, methodological categories, real-world applications, and future research directions, aiming to fill the gap in existing literature and serve as a foundational reference for advancing fine-grained land change analysis beyond traditional binary detection.
2021 Change Detection From Very-High-Spatial-Resolution Optical Remote Sensing Images: Methods, applications, and future directions GRSM2021 Provides a comprehensive overview of change detection in very-high-spatial-resolution (≤5 m) remote sensing images, systematically examining current methods, real-world applications, and future research directions to address challenges such as limited spectral information, spectral variability, and geometric distortions.
2020 A survey of change detection methods based on remote sensing images for multi-source and multi-objective scenarios RS2020 Surveys change detection methods for multi-source remote sensing images and multi-objective scenarios, summarizing a general framework including change information extraction, data fusion, and analysis, and discusses future directions.
2020 Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges RS2020 Reviews the state-of-the-art methods, applications, and challenges of AI for change detection, covering data sources, deep learning frameworks, and unsupervised schemes, and discusses issues like heterogeneous data processing and AI reliability. Updates are available at MinZHANG-WHU/Change-Detection-Review.
2019 A Review of Change Detection in Multitemporal Hyperspectral Images: Current Techniques, Applications, and Challenges GRSM2019 Presents a comprehensive review of change detection in hyperspectral remote sensing images, covering fundamental concepts, methodological categories, current techniques, and key challenges, while demonstrating state-of-the-art approaches through experimental results to highlight the unique potential and complexity of exploiting high spectral resolution for fine-scale land-cover change monitoring.
2018 多时相遥感影像变化检测方法综述 武汉大学学报 (信息科学版) 2018 本文系统回顾了多时相遥感影像变化检测技术的发展历程，从预处理、方法分类到精度评价全面梳理研究进展，指出当前尚无普适性通用方法，并分析核心难点与应对策略，旨在推动该领域向更深入、更系统方向发展。
2017 多时相遥感影像变化检测的现状与展望 测绘学报2017 本文围绕多时相遥感影像变化检测的基本流程，从预处理、方法、阈值分割到精度评价系统梳理最新研究进展，总结其在生态环境监测与城市发展等领域的应用，并展望高光谱与高分辨率影像驱动下的未来发展方向。
2017 Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications ISPRS P&RS 2017 Reviews change detection studies based on Landsat time series, covering frequencies, preprocessing steps, algorithms, and applications, and discusses the impact of free access to Landsat data on change detection methodologies.
2016 Optical remotely sensed time series data for land cover classification: A review ISPRS P&RS 2016 Reviews the use of optical remote sensing time series data for land cover classification, discussing issues and opportunities in generating annual land cover products and methods for incorporating time series information.
2016 SAR影像变化检测研究进展 计算机研究与发展2015 本文系统梳理了SAR影像变化检测的经典流程与传统方法，重点综述近年来在差异图生成及阈值、聚类、图切、水平集等分析方法上的新兴算法改进，并通过两组数据集定量验证其性能，最后展望了该领域仍需深入研究的关键方向。
2015 A critical synthesis of remotely sensed optical image change detection techniques RSE2015 Provides a critical synthesis of remote sensing change detection techniques, organizing the literature by unit of analysis and comparison method to reduce conceptual overlap and guide future research.
2013 Change detection from remotely sensed images: From pixel-based to object-based approaches ISPRS P&RS 2013 Reviews change detection methodologies from pixel-based to object-based approaches, discussing the potential of object-based methods and data mining techniques with the advent of very-high-resolution imagery.
2012 Object-based change detection IJRS2012 Discusses object-based change detection (OBCD) using high-spatial-resolution imagery, comparing it with pixel-based approaches and reviewing algorithms and applications for detailed change information extraction.
2012 A review of large area monitoring of land cover change using Landsat data RSE2012 Reviews methods for large area monitoring of land cover change using Landsat data, focusing on forest cover change, and discusses radiometric correction, temporal updating, and the impact of free access to terrain-corrected data.
2011 多时相遥感影像变化检测综述 地理信息世界2011 本文系统回顾多时相遥感影像变化检测的发展现状，从环境变化特性出发，围绕预处理、方法分类、精度评估等四大方面梳理技术演进，并提出融合多源数据、集成处理与智能方法的综合解决方案，同时指出当前挑战与应对策略，以推动该领域深入发展。
2005 Image change detection algorithms: a systematic survey TIP2004 Provides a systematic survey of image change detection algorithms, covering common processing steps and core decision rules, and discusses preprocessing methods, consistency enforcement, and performance evaluation principles.
2004 Digital change detection methods in ecosystem monitoring: a review IJRS2004 Reviews digital change detection methods in ecosystem monitoring, covering multi-temporal, multi-spectral data techniques, preprocessing routines, and change detection algorithms, and highlights the complementarity between different methods.
2004 Change detection techniques IJRS2004 Summarizes and reviews change detection techniques using remote sensing data, highlighting image differencing, principal component analysis, and post-classification comparison as common methods, and discusses emerging techniques like spectral mixture analysis and neural networks.
2003 利用遥感影像进行变化检测 武汉大学学报 (信息科学版) 2003 本文针对遥感影像变化检测的紧迫需求与技术难点，提出影像配准与变化检测同步求解的新思路，并探讨其拓展至三维变化检测的可行性，系统比较七类主流方法，最后指明未来重点研究方向。

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