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Development","sub_category":"Curated Lists","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"# \u003cp align=center\u003e`Awesome Remote Sensing Change Detection`\u003c/p\u003e\n[![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome) [![Maintenance](https://img.shields.io/badge/Maintained%3F-yes-green.svg)](https://GitHub.com/Naereen/StrapDown.js/graphs/commit-activity) [![PR's Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat)](http://makeapullrequest.com) [![made-with-Markdown](https://img.shields.io/badge/Made%20with-Markdown-1f425f.svg)](http://commonmark.org)![Forks](https://img.shields.io/github/forks/wenhwu/awesome-remote-sensing-change-detection?style=social)![GitHub stars](https://img.shields.io/github/stars/wenhwu/awesome-remote-sensing-change-detection?style=social)![Last Commit](https://img.shields.io/github/last-commit/wenhwu/awesome-remote-sensing-change-detection.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)\n\nA comprehensive and up-to-date compilation of datasets, tools, methods (including foundation models, diffusion models, transformers, and CNNs), review papers, and competitions for remote sensing change detection.\n\n# Contents\n\n- [Datasets](#datasets)\n  - [Optical Datasets](#optical-datasets)\n  - [Multi-Modal and SAR Datasets](#multi-modal-and-sar-datasets)\n- [Tools](#tools)\n- [Methods](#methods)\n  - [Deep Learning](#deep-learning)\n    - [Foundation Models](#foundation-models)\n    - [Diffusion Models and GANs](#diffusion-models-and-gans)\n    - [Transformers](#transformers)\n    - [CNNs](#cnns)\n  - [Traditional Methods](#traditional-methods)\n    - [Common Methods](#common-methods)\n    - [New Methods](#new-methods)\n- [Review Papers](#review-papers)\n- [Competitions](#competitions)\n- [Satellite Data Resources for Disaster Response](#satellite-data-resources-for-disaster-response)\n- [More Resources](#more-resources)\n- [Citation](#citation)\n\n\n# Datasets\n\n* SCD: Semantic Change Detection, BCD: Binary Change Detection, DDA: Disaster Damage Assessment, BDA: Building Damage Assessment, RSICC: Remote Sensing Image Change Captioning\n\n## Optical Datasets\n\n|Year|Task|Target| Dataset |Publication|Source|Image Pairs |Image Size|Resolution|Location|Class|\n|:---|:--- |:--- | :------| :------|:----------| :-------| :-------| :----------- | :----- | :---- |\n|2025|SCD+BCD|Building|[RSCC](https://github.com/Bili-Sakura/RSCC)|[NeurIPS2025](https://openreview.net/forum?id=yn2fJYBKEB)|Open Maxar Data Programme |62,351|512 × 512|0.3-0.8m|31 Locations Globally|5|\n|2026|SCD|Land cover|[LsSCD-Ex](https://github.com/tangkai-RS/DreamCD)|[JAG2026](https://doi.org/10.1016/j.jag.2026.105125)|Google Earth|100|2048 × 2048|0.6m|Nanjing, China|8|\n|2026|SCD+BCD|Building|[FOTBCD-Binary;FOTBCD-Instances](https://github.com/abdelpy/FOTBCD-datasets)|[arXiv2026](https://arxiv.org/abs/2601.22596)|IGN|27,871;4000|512 × 512|0.2m|France|2;3|\n|2025|RSICC|Land cover|[MOSAIC-SEN2-CC](https://github.com/ChangeCapsInRS/MOSAIC-SEN2-CC)|[JSTARS2025](https://ieeexplore.ieee.org/document/11181102/)|Sentinel-2|5,232|480 × 480|10m|Global|8|\n|2025|SCD|Cropland|[Xiamen](https://github.com/long123524/CPGNet), [Fuzhou](https://github.com/long123524/HGINet-torch)|[JAG2025](https://www.sciencedirect.com/science/article/pii/S1569843225005631?dgcid=rss_sd_all), [ISPRS P\u0026RS 2024](https://www.sciencedirect.com/science/article/pii/S0924271624001709)|Google Earth|6480 (Xiamen); 8719 (Fuzhou)|256 × 256|0.5m|Xiang’an and Tong’an districts, Xiamen, China; Changle and Minhou districts, Fuzhou, China|7|\n|2025|SCD+BCD|Land cover|[WHU-GCD](https://gpcv.whu.edu.cn/data/WHU_Generative_Change_Detection_Dataset.html)|[ISPRS P\u0026RS 2025](https://www.sciencedirect.com/science/article/pii/S0924271625001595?dgcid=rss_sd_all)|LoveDA, Evlab-SS, LandCover.ai, and Google Earth; DSIFN-CD,LEVIR-CD, SECOND, CLCD, CNAM-CD|28,067|512 × 512|-|Global|26|\n|2025|BCD|Building|[CWSCD](https://github.com/yuruqingsi/CWSCD-dataset)|[JAG2025](https://www.sciencedirect.com/science/article/pii/S1569843225005084?dgcid=rss_sd_all)|BJ-2, GF-2|200|2048×2048|1m|Hebei, China|2|\n|2025|BCD|Building|DVCD|[arXiv2025](https://arxiv.org/abs/2506.17944)|Drone Images|12,833|-|0.1m|Guangdong, China|2|\n|2025|SCD|Land cover|[SC-SCD7, CC-SCD5](https://github.com/StephenApX/MTL-TripleS)|[ISPRS P\u0026RS 2025](https://www.sciencedirect.com/science/article/pii/S0924271625003776?dgcid=rss_sd_all)|Pléiades, Beijing-2, Gaofen-1, Gaofen-2, Ziyuan-3|1,722; 953|512×512|0.5m, 2.3m, 2.5 m|Zhangzhou (Longwen) and Henan (Dengfeng, Luoyang, Sanmenxia), China |8; 5|\n|2025|SCD|Land cover|[LevirSCD](https://github.com/zmoka-zht/FoBa)|[arXiv2025](https://arxiv.org/abs/2509.15788)|GF-1, Google Earth|3,225|256×256|1-2|Beijing, China|16|\n|2025|BCD|Land cover|[JL1-CD](https://github.com/circleLZY/MTKD-CD)|[arXiv2025](https://arxiv.org/pdf/2502.13407)|Jilin-1|5,000|512×512|0.5-0.75m|Multiple provinces in China|2|\n|2025|SCD|Building|[EBD](https://figshare.com/articles/figure/An_Extended_Building_Damage_EBD_dataset_constructed_from_disaster-related_bi-temporal_remote_sensing_images_/25285009/2)| [JRS2025](https://spj.science.org/doi/full/10.34133/remotesensing.0733?af=R)|WorldView-3|\u003e18,000|512×512|0.3-0.5m|Global|7|\n|2025|SCD|Land use|[MLCD](https://aistudio.baidu.com/dataset/detail/245516/intro)|[JSTARS2025](https://ieeexplore.ieee.org/document/11058393)|Google Earth Engine|10, 000|256×256|0.5-2m|Macao, China|\n|2024|BCD|Mine| [MineNetCD](https://huggingface.co/datasets/HZDR-FWGEL/MineNetCD256) |[TGRS2024](https://ieeexplore.ieee.org/document/10744421) |Google Earth|71,711|256×256| 1.2m |Global|2|\n|2024|BCD|Building| [TUE-CD](https://github.com/RSMagneto/MSI-Net)| [TGRS2024](https://ieeexplore.ieee.org/document/10623278)|WorldView-2|1,656|256×256|1.8m|Turkey|2|\n|2024|SCD|Urban|[MSRS-CD](https://github.com/bobo59/MSRSCD)|[JSTARS2024](https://ieeexplore.ieee.org/document/10813409)|-|841|1,024×1,024|0.5m|Southern Chinese cities|5|\n|2024|SCD|Cropland| [CropSCD](https://github.com/lsmlyn/CropSCD)| [TGRS2024](https://ieeexplore.ieee.org/document/10579791) |-|4,141|512×512|0.5-2m|Guangdong, China|9|\n|2024|SCD|Cropland| [Hi-CNA](https://rsidea.whu.edu.cn/Hi-CNA_dataset.htm) |[ISPRS P\u0026RS 2024](https://www.sciencedirect.com/science/article/pii/S0924271624002090) |GF-2|6,797| 512×512|0.8m|China (Hebei, Shanxi, Shandong, and Hubei) |5|\n|2024|SCD|Land cover|[ChangNet](https://github.com/jankyee/ChangeNet)| [ICASSP2024](https://ieeexplore.ieee.org/document/10446592)|WayBack|31,000|1,900×1,200|0.3m|100 Cities in China|6|\n|2023|SCD|Cropland|[JL1](https://www.jl1mall.com/resrepo/?fromUrl=https://www.jl1mall.com/edu)|-|Jilin-1|8,000 | 256×256 |\u003c0.75m|-|9|\n|2023|BCD|Building| [EGY-BCD](https://github.com/oshholail/EGY-BCD)| [GRSL2023](https://ieeexplore.ieee.org/document/10145434)|Google Earth |6,091 | 256×256| 0.25m| Egypt|2|\n|2023|BCD|Building| [HRCUS-CD](https://github.com/zjd1836/AERNet)| [TGRS2023](https://ieeexplore.ieee.org/document/10209204)|-|11,388 |256×256| 0.5m|Zhuhai, China|2|\n|2023|BCD|Building| [SI-BU](https://vrlab.org.cn/~hanhu/projects/bcenet/)| [ISPRS P\u0026RS 2023](https://www.sciencedirect.com/science/article/pii/S0924271623001284?via%3Dihub)|Google Earth|4,932|512×512| 0.2m| Guiyang, China|2|\n|2023|SCD|Land cover|[CNAM-CD](https://github.com/Silvestezhou/CNAM-CD)| [RS2023](https://www.mdpi.com/2072-4292/15/9/2464)|Google Earth|2,503|512×512|0.5m|12 State-level New Areas in China|6|\n|2023|SCD|Land cover| [WUSU](https://rsidea.whu.edu.cn/resource_wusu_sharing.htm)| [IJDE2023](https://www.tandfonline.com/doi/full/10.1080/17538947.2023.2246445)|GF-2|3| 6,358×6,382 / 7,025×5,500| 1m |Wuhan, China|12|\n|2023|BCD|Landslide| [GVLM](https://github.com/zxk688/GVLM)| [ISPRS P\u0026RS 2023](https://www.sciencedirect.com/science/article/pii/S0924271623000242)|Google Earth|17| 1,748×1,748-10,808×7,424|0.59m|Global|2|\n|2023|SCD|Building|[BANDON](https://github.com/fitzpchao/BANDON)| [Sci. China Inf. Sci. 2023](https://link.springer.com/article/10.1007/s11432-022-3691-4)|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|\n|2023|SCD|Land cover| [DynamicEarthNet](https://mediatum.ub.tum.de/1650201) | [CVPR2022](https://openaccess.thecvf.com/content/CVPR2022/html/Toker_DynamicEarthNet_Daily_Multi-Spectral_Satellite_Dataset_for_Semantic_Change_Segmentation_CVPR_2022_paper.html) |PlanetFusion|54,750|1,024×1,024|3m|Global|7|\n|2022|BCD|Road|[CRCD, WRCD](http://www.lmars.whu.edu.cn/suihaigang/index)|[TITS2022](https://ieeexplore.ieee.org/abstract/document/9815123)|Aerial Images, Google Earth|3,237, 1,960|512×512|0.2m, 1.14m|Christchurch, New Zealand; Jiangxia, Wuhan, China|2|\n|2022|BCD|Cropland| [CLCD](https://github.com/liumency/CropLand-CD)| [JSTARS2022](https://ieeexplore.ieee.org/document/9780164)|GF-2|600|512×512|0.5-2m|Guangdong, China|2|\n|2022|RSICC|Building | [LEVIR-CC](https://github.com/Chen-Yang-Liu/RSICC)  | [TGRS2022](https://ieeexplore.ieee.org/document/9934924)|Google Earth|10,077| 1,024×1,024| 0.5m| Texas, USA|2|\n|2022|BCD|Land cover | [SYSU-CD](https://github.com/liumency/SYSU-CD)    | [TGRS2021](https://ieeexplore.ieee.org/document/9467555) |-|20,000| 256×256 | 0.5m |Hong Kong, China |2|\n|2022|SCD|Building| [S2Looking](https://github.com/S2Looking/Dataset)|[RS2021](https://www.mdpi.com/2072-4292/13/24/5094) |GF, SuperView, BJ-2|5,000| 1,024×1,024| 0.5-0.8m| Global|2|\n|2022|BCD|Building| [LEVIR-CD+](https://github.com/S2Looking/Dataset)|[RS2021](https://www.mdpi.com/2072-4292/13/24/5094) |Google Earth|985|1,024×1,024|0.5m|Texas, USA|2|\n|2022|SCD|Land cover| [Landsat-SCD](https://figshare.com/articles/figure/Landsat-SCD_dataset_zip/19946135/1)|[IJDE2022](https://www.tandfonline.com/doi/full/10.1080/17538947.2022.2111470) |Landsat|8,468|416×416|30m|Xinjiang, China|10|\n|2022|SCD|Building|[NanjingDataset](https://github.com/SianGIS/NanjingDataset)|[ISPRS P\u0026RS 2022](https://www.sciencedirect.com/science/article/pii/S0924271622001344) |Google Earth|2,519|256×256|0.3m|Nanjing, China|3|\n|2022|RSICC|Urban|[Dubai-CC](https://disi.unitn.it/~melgani/datasets.html)|[TGRS2022](https://ieeexplore.ieee.org/document/9847254)|Landsat 7|500|50×50|30m|Dubai|6|\n|2022|SCD|Flood|[SpaceNet 8](https://join.topcoder.com/spacenet) | [CVPR2022W](https://openaccess.thecvf.com/content/CVPR2022W/EarthVision/papers/Hansch_SpaceNet_8_-_The_Detection_of_Flooded_Roads_and_Buildings_CVPRW_2022_paper.pdf)|Maxar| 12 | 1,300×1,300 | 0.3-0.8m |Germany, and Louisiana|4|\n|2021|SCD|Land cover|[MSD](https://www.grss-ieee.org/community/technical-committees/2021-ieee-grss-data-fusion-contest-track-msd/)|[JSTARS2022](https://ieeexplore.ieee.org/document/9690575)|NAIP, Landsat-8, and NLCD|2,250|-|1m, 30m|Maryland, USA|16|\n|2021|SCD|Land cover| [S2MTCP](https://zenodo.org/records/4280482)|[ICPR2021](https://link.springer.com/chapter/10.1007/978-3-030-68787-8_42) |Sentinel-2|1,520|600×600|10m|Global|-|\n|2021|BCD|Urban|[HTCD](https://github.com/ShaoRuizhe/SUNet-change_detection) |[RS2021](https://www.mdpi.com/2072-4292/13/18/3750/htm)|Google Earth, Open Aerial Map|3,772|256×256, 2,048×2,048|0.5971m, 0.07465m|Chisinau, Moldova|2|\n|2020|BCD|Building| [GZ-CD](https://github.com/daifeng2016/Change-Detection-Dataset-for-High-Resolution-Satellite-Imagery) (or CD_Data_GZ)|[TGRS2020](https://ieeexplore.ieee.org/document/9161009)|Google Earth|19| 1,006×1,168-4,936×5,224| 0.55m| Guangzhou, China|2|\n|2020|BCD|Building| [DSIFN](https://github.com/GeoZcx/A-deeply-supervised-image-fusion-network-for-change-detection-in-remote-sensing-images/tree/master/dataset) (or DSIFN-CD)|[ISPRS P\u0026RS 2020](https://www.sciencedirect.com/science/article/pii/S0924271620301532?via%3Dihub) |Google Earth|3,940 | 512×512|-|China (Beijing, Chengdu, Shenzhen, Chongqing, Wuhan, and Xian)|2|\n|2020|BCD|Building| [LEVIR-CD](https://justchenhao.github.io/LEVIR/)| [RS2020](https://www.mdpi.com/2072-4292/12/10/1662)|Google Earth|637| 1,024×1,024|0.5m|Texas, USA|2|\n|2020|SCD|Land cover| [Hi-UCD](https://github.com/Daisy-7/Hi-UCD-S?tab=readme-ov-file)|[arXiv2020](https://arxiv.org/abs/2011.03247) |Aerial Images|1,293|1,024×1,024|0.1m|Tallinn, Estonia|9|\n|2020|SCD|Land cover| [SECOND](https://captain-whu.github.io/SCD/)| [TGRS2021](https://ieeexplore.ieee.org/document/9555824) |Aerial Images |4,662|512×512| -  |China (Hangzhou, Chengdu, and Shanghai)|6|\n|2020|BCD| Building | [MUDS](https://medium.com/the-downlinq/the-spacenet-7-multi-temporal-urban-development-challenge-dataset-release-9e6e5f65c8d5) (or SpaceNet 7) | [CVPR2021](https://openaccess.thecvf.com/content/CVPR2021/papers/Van_Etten_The_Multi-Temporal_Urban_Development_SpaceNet_Dataset_CVPR_2021_paper.pdf)|-| -|1,024×1,024|4m|Global|2|\n|2019|BDA|Building| [xBD](https://xview2.org/dataset) | [arXiv2019](https://arxiv.org/abs/1911.09296) |Maxar| 11,034 | 1,024×1,024 | \u003c0.8m |Global|4|\n|2019|SCD|Land cover| [HRSCD](https://ieee-dataport.org/open-access/hrscd-high-resolution-semantic-change-detection-dataset)| [CVIU2019](https://www.sciencedirect.com/science/article/pii/S1077314219300992) |IGN|291|10,000×10,000|0.5m|France (Rennes, and Caen)|5|\n|2018|BCD|Building| [WHU-CD](https://study.rsgis.whu.edu.cn/pages/download/building_dataset.html)| [TGRS2018](https://ieeexplore.ieee.org/document/8444434)|Aerial Image|1|32,507×15,354|0.2m| Christchurch, New Zealand|2|\n|2018|BCD|Building| [CDD](https://drive.google.com/file/d/1GX656JqqOyBi_Ef0w65kDGVto-nHrNs9/edit?pli=1) (or SVCD)| [Int. Arch. Photogramm. Remote Sens. Spatial Inf. 2018](https://isprs-archives.copernicus.org/articles/XLII-2/565/2018/isprs-archives-XLII-2-565-2018.pdf) |Google Earth|1,6000| 256×256| 0.03-1m |-|2|\n|2018|BCD|Riverway| [The River Data Set](https://share.weiyun.com/5xdge4R)|[TGRS2018](https://ieeexplore.ieee.org/document/8418840)|EO-1 Hyperion|1|463×241|30m|Jiangsu, China|2|\n|2018|BCD|Land cover|[OSCD](https://ieee-dataport.org/open-access/oscd-onera-satellite-change-detection)|[IGARSS2018](https://ieeexplore.ieee.org/document/8518015)|Sentinel-2|24|600×600|10-60m|Global|2|\n|2008|BCD|Land cover|[SZTAKI](http://web.eee.sztaki.hu/remotesensing/airchange_benchmark.html)|[TGRS2009](https://ieeexplore.ieee.org/document/5169964)|Aerial Images|13|952x640|1.5m|-|\n\n\n## Multi-Modal and SAR Datasets\n\n|Year|Task|Target| Dataset |Publication|Source|Image Pairs |Image Size|Resolution|Location|Class|\n|:---|:---|:--- | :------| :------|:----------| :-------| :-------| :----------- | :----- | :---- |\n|2025|DDA|Disaster|[DisasterM3](https://github.com/Junjue-Wang/DisasterM3)|[NeurIPS2025](https://arxiv.org/abs/2505.21089)| Optical-SAR-Instruction |-|-|-|Global|-|\n|2025|SCD|Building| [BRIGHT](https://github.com/ChenHongruixuan/BRIGHT) | [arXiv2025](https://arxiv.org/abs/2501.06019) |Optical and SAR|4,538| 1,024×1,024| 0.3-1m|Global|4|\n|2024|SCD|Building| [Hi-BCD](https://github.com/HATFormer/MMCD) | [Information Fusion 2023](https://www.sciencedirect.com/science/article/pii/S1566253524001362) |Aerial Images, DSMs|1,500| 1,000×1,000 |0.25m |Netherlands (Amsterdam, Rotterdam, and Utrecht)|3|\n|2024|SCD|Flood |[UrbanSARFloods](https://github.com/jie666-6/UrbanSARFloods)|[CVPR2024W](https://openaccess.thecvf.com/content/CVPR2024W/EarthVision/html/Zhao_UrbanSARFloods_Sentinel-1_SLC-Based_Benchmark_Dataset_for_Urban_and_Open-Area_Flood_CVPRW_2024_paper.html) | Sentinel-1|8,879| 512×512|20m|Global|5|\n|2024|SCD|Land use |[EVLab-CMCD](https://github.com/whudk/EVLab-CMCD) | [ISPSR P\u0026RS 2024](https://www.sciencedirect.com/science/article/pii/S0924271624003873)|GF-2, BJ-2, Historical land use maps|5,622|512×512| 0.8m| 10 Cities in China|13|\n|2023|BCD|Flood |[CAU-Flood](https://github.com/CAU-HE/CMCDNet)| [JAG2023](https://www.sciencedirect.com/science/article/pii/S1569843223000195) |Sentinel-1, Sentinel-2|18,302| 256×256|10m|Global|2|\n|2023|SCD|Flood|[Kuro Siwo](https://github.com/Orion-AI-Lab/KuroSiwo)|[NeurIPS2024](https://proceedings.neurips.cc/paper_files/paper/2024/hash/43612b0662cb6a4986edf859fd6ebafe-Abstract-Datasets_and_Benchmarks_Track.html)|Sentinel-1, DEM| 67,490 |224×224| 10m|Global|3|\n|2023|SCD|Urban|[SMARS](https://www.dlr.de/en/eoc/about-us/remote-sensing-technology-institute/photogrammetry-and-image-analysis/public-datasets/smars)| [ISPRS P\u0026RS 2023](https://www.sciencedirect.com/science/article/pii/S092427162300254X)|Simulated Orthoimages and DSMs|-|512×512|0.3m, 0.5m|Simulated Paris and Venice|3|\n|2023|BCD|Urban|[3DCD](https://sites.google.com/uniroma1.it/3dchangedetection/home-page?pli=1) |[ISPRS P\u0026RS 2023](https://www.sciencedirect.com/science/article/pii/S0924271622003240)|Aerial Images, DSMs|472|400×400, 200×200|0.5m, 1m|Valladolid, Spain|2| \n|2023|SCD|Urban|[Urb3DCD–V2](https://ieee-dataport.org/open-access/urb3dcd-urban-point-clouds-simulated-dataset-3d-change-detection)|[ISPRS P\u0026RS 2023](https://www.sciencedirect.com/science/article/pii/S0924271623000394)|ALS, Multi-Sensor|-|-|-|Simulated|7|\n|2022|BCD|Flood| [Wuhan](https://github.com/GeoZcx/A-Domain-Adaption-Neural-Network-for-Change-Detection-with-Heterogeneous-Optical-and-SAR-Remote-Sens/tree/main/data) | [JAG2022](https://www.sciencedirect.com/science/article/pii/S0303243422000952) |Sentinel-2, COSMO-SkyMed|1| 11,216×13,693|3m |Wuhan, China|2|\n|2022|BCD|Flood|[Ombria](https://github.com/geodrak/OMBRIA) |[JSTARS2022](https://ieeexplore.ieee.org/document/9723593/) |Sentinel-1, Sentinel-2| 1,688| 256×256| 10m|Global|2|\n|2021|BCD|Land cover|[MultiModalOSCD](https://github.com/PatrickTUM/multimodalCD_ISPRS21)|[ISPRS. XXIV ISPRS Congress 2021](https://isprs-archives.copernicus.org/articles/XLIII-B3-2021/243/2021/isprs-archives-XLIII-B3-2021-243-2021.pdf)|Sentinel-1, Sentinel-2|24|600×600|10-60m|Global|2|\n\n# Tools\n\n| Year | Abbreviation | Description | Other|\n| :--- | :--- | :--- | :--- |\n|2024|[rschange](https://github.com/xwmaxwma/rschange)| An open-source toolbox dedicated to reproducing and developing advanced methods (e.g., DDLNet, CDMask) for change detection in remote sensing images.|![Last Commit](https://img.shields.io/github/last-commit/xwmaxwma/rschange.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/xwmaxwma/rschange?style=social)|\n|2024|[torchange](https://github.com/Z-Zheng/pytorch-change-models)| 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 Commit](https://img.shields.io/github/last-commit/Z-Zheng/pytorch-change-models.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/Z-Zheng/pytorch-change-models?style=social)|\n|2022| [Open-CD](https://github.com/likyoo/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](https://arxiv.org/abs/2407.15317). |![Last Commit](https://img.shields.io/github/last-commit/likyoo/open-cd.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/likyoo/open-cd?style=social)|\n|2022|[PaddleRS](https://github.com/PaddlePaddle/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](https://github.com/Bobholamovic/CDLab).|![Last Commit](https://img.shields.io/github/last-commit/PaddlePaddle/PaddleRS.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/PaddlePaddle/PaddleRS?style=social)|\n|2020|[Change Detection Repository](https://github.com/ChenHongruixuan/ChangeDetectionRepository)|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 Commit](https://img.shields.io/github/last-commit/ChenHongruixuan/ChangeDetectionRepository.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/ChenHongruixuan/ChangeDetectionRepository?style=social)|\n|2019|[ChangeDetectionToolbox](https://github.com/Bobholamovic/ChangeDetectionToolbox)|This MATLAB toolbox provides a modular, end-to-end framework for remote sensing change detection, implementing key methods such as CVA , MAD , and IRMAD to generate difference images and evaluate change maps.|![Last Commit](https://img.shields.io/github/last-commit/Bobholamovic/ChangeDetectionToolbox.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/Bobholamovic/ChangeDetectionToolbox?style=social)|\n\n\n# Methods\n\n## Deep Learning\n\n### Foundation Models\n\n| Year | Abbreviation | Title | Publication |Foundation Models| Keywords | Experiment Datasets |Other|\n| :--- | :--- | :------| :--- | :--- | :--- |:------- |:------- |\n|2025|DaCDF|A change detection framework with relative depth information assistance|[JAG2025](https://www.sciencedirect.com/science/article/pii/S1569843225005898?dgcid=rss_sd_all)|Depth Anything|Depth-Anything; Multi-task learning|WHU-CD, LEVIR-CD, SECOND|-|\n|2025|[GeoVLM-R1](https://github.com/mustansarfiaz/GeoVLM-R1-Toolkit)|GeoVLM-R1: Reinforcement Fine-Tuning for Improved Remote Sensing Reasoning|[arXiv2025](https://arxiv.org/abs/2509.25026)|Qwen2.5VL-3B-Instruct|Task aware rewards, reasoning based RL models|GeoChat-Instruct, NWPU VHR-10; Dubai-CC, LEVIR-MCI, MUDS, SYSU-CD; NWPU-Captions, RSCID-Captions, RSITMD-Captions|![Last Commit](https://img.shields.io/github/last-commit/mustansarfiaz/GeoVLM-R1-Toolkit.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/mustansarfiaz/GeoVLM-R1-Toolkit?style=social)|\n|2025|ChangeVG|Towards Comprehensive Interactive Change Understanding in Remote Sensing: A Large-scale Dataset and Dual-granularity Enhanced VLM|[arXiv2025](https://arxiv.org/abs/2509.23105)|Qwen2.5-VL-7B|Remote sensing change understanding, Interactive multi-task instruction dataset, Vision-language models|ChangeIMTI (constructed from LEVIR-CC, LEVIR-MCI)|-|\n|2025|[SegChange-R1](https://github.com/Yu-Zhouz/SegChange-R1)|SegChange-R1: LLM-Augmented Remote Sensing Change Detection|[arXiv2025](https://arxiv.org/abs/2506.17944)|Swin Transformer, Microsoft/Phi-1.5|LLM-augmented inference approach, Linear attention-based spatial transformation module|WHU-CD, CDD, DSIFN-CD, DVCD|![Last Commit](https://img.shields.io/github/last-commit/Yu-Zhouz/SegChange-R1.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/Yu-Zhouz/SegChange-R1?style=social)|\n|2025|[SAM2-CD](https://github.com/KimotaQY/SAM2-CD)|SAM2-CD: Remote Sensing Image Change Detection With SAM2|[JSTAR2025](https://ieeexplore.ieee.org/document/11164661)|SAM2|Dynamic feature selection, global–local attention|WHU-CD, LEVIR-CD, and LEVIR-CD+|![Last Commit](https://img.shields.io/github/last-commit/KimotaQY/SAM2-CD.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/KimotaQY/SAM2-CD?style=social)|\n|2025|[ViTP](https://github.com/zcablii/ViTP)|Visual Instruction Pretraining for Domain-Specific Foundation Models|[arXiv2025](https://arxiv.org/abs/2509.17562)|ViT, InternVL-2.5|Leveraging reasoning to enhance perception, ViT, Visual Robustness Learning|16 challenging remote sensing and medical imaging benchmarks |![Last Commit](https://img.shields.io/github/last-commit/zcablii/ViTP.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/zcablii/ViTP?style=social)|\n|2025|[AdaptVFMs-RSCD](https://github.com/Jiang-CHD-YunNan/RS-VFMs-Fine-tuning-Dataset)|AdaptVFMs-RSCD: Advancing Remote Sensing Change Detection from binary to semantic with SAM and CLIP|[ISPRS P\u0026RS 2025](https://www.sciencedirect.com/science/article/pii/S0924271625003636?dgcid=rss_sd_all)|CLIP, SAM|Remote sensing VFM fine-tuning dataset|RS VFM Fine-tuning dataset, DSIFN-CD, CLCD, SYSU-CD|![Last Commit](https://img.shields.io/github/last-commit/Jiang-CHD-YunNan/RS-VFMs-Fine-tuning-Dataset.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/Jiang-CHD-YunNan/RS-VFMs-Fine-tuning-Dataset?style=social)|\n|2025|[PeftCD](https://github.com/dyzy41/PeftCD)|PeftCD: Leveraging Vision Foundation Models with Parameter-Efficient Fine-Tuning for Remote Sensing Change Detection|[arXiv2025](https://arxiv.org/abs/2509.09572)|SAM2, DINOv3|Vision Foundation Models, Parameter-Efficient Fine-Tuning|WHU-CD, CDD, LEVIR-CD, SYSU-CD, MSRSCD, MLCD, S2Looking|![Last Commit](https://img.shields.io/github/last-commit/dyzy41/PeftCD.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/dyzy41/PeftCD?style=social)|\n|2025|DepthCD|Balanced Diffusion-Guided Fusion for Multimodal Remote Sensing Classification|[ISPRS P\u0026RS 2025](https://www.sciencedirect.com/science/article/pii/S0924271625002072?dgcid=rss_sd_all)|ViT|Depth prompt, Dimensional correlation of change, Lightweight adapter, Binary change detection, Semantic change detection|SECOND,LandsatSCD, HiUCDs; SYSU-CD, HRCUS-CD, WRCD|-|\n|2025|[SA-CDNet](https://github.com/DREAMXFAR/SA-CDNet)|Detect Changes Like Humans: Incorporating Semantic Priors for Improved Change Detection|[TGRS2025](https://ieeexplore.ieee.org/document/11159523)|FastSAM|Dual-stream decoder, multiscale feature, visual foundation model|AIRS, INRIA-Building, and WHU-Building; DLCCC, and LoveDA; WHU-CD, LEVIR-CD, LEVIR-CD+, S2Looking, WHU Cultivate Land Dataset |![Last Commit](https://img.shields.io/github/last-commit/DREAMXFAR/SA-CDNet.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/DREAMXFAR/SA-CDNet?style=social)|\n| 2025 | [DynamicEarth](https://github.com/likyoo/DynamicEarth) | DynamicEarth: How Far are We from Open-Vocabulary Change Detection?  | [arXiv2025](https://arxiv.org/abs/2501.12931)|SAM2, DINOv2|  Open-Vocabulary Change Detection| WHU-CD, LEVIR-CD, SECOND, S2Looking, and BANDON|![Last Commit](https://img.shields.io/github/last-commit/likyoo/DynamicEarth.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/likyoo/DynamicEarth?style=social)|\n|2025|[DisasterM3](https://github.com/Junjue-Wang/DisasterM3)| DisasterM3: A Remote Sensing Vision-Language Dataset for Disaster Damage Assessment and Response | [NeurIPS2025](https://arxiv.org/abs/2505.21089)|LLaVA, Kimi, InternVL, Qwen2.5, GeoCha, TeoChat, EarthDial, GPT4|Multi-hazard, Multi-sensor, Multi-task|DisasterM3|![Last Commit](https://img.shields.io/github/last-commit/Junjue-Wang/DisasterM3.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/Junjue-Wang/DisasterM3?style=social)|\n|2025|[EFI-SAM](https://github.com/juncyan/efi-sam)|SAM-Based Efficient Feature Integration Network for Remote Sensing Change Detection: A Case Study on Macao Sea Reclamation|[JSTARS2025](https://ieeexplore.ieee.org/document/11058393/)|SAM|Random Fourier features, sea reclamation|CLCD, SYSU-CD, S2Looking, MLCD|![Last Commit](https://img.shields.io/github/last-commit/juncyan/efi-sam.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/juncyan/efi-sam?style=social)|\n| 2024 | [AnyChange](https://github.com/Z-Zheng/pytorch-change-models/blob/main/torchange/models/segment_any_change) |Segment Any Change | [NeurIPS2024](https://proceedings.neurips.cc/paper_files/paper/2024/file/9415416201aa201902d1743c7e65787b-Paper-Conference.pdf) | SAM| Zero-shot change detection, bitemporal latent matching|xBD, LEVIR-CD, S2Looking, SECOND  | ![Last Commit](https://img.shields.io/github/last-commit/Z-Zheng/pytorch-change-models.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/Z-Zheng/pytorch-change-models?style=social) |\n|2024|[SCM](https://github.com/StephenApX/UCD-SCM)|Segment Change Model (SCM) for Unsupervised Change detection in VHR Remote Sensing Images: a Case Study of Buildings|[IGARSS 2024](https://ieeexplore.ieee.org/document/10642429)|SAM, CLIP|Unsupervised Change Detection, Vision Foundation Model|WHU-CD, LEVIR-CD|![Last Commit](https://img.shields.io/github/last-commit/StephenApX/UCD-SCM.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/StephenApX/UCD-SCM?style=social) |\n|2024|[SemiCD-VL](https://github.com/likyoo/SemiCD-VL) |SemiCD-VL: Visual-Language Model Guidance Makes Better Semi-Supervised Change Detector| [TGRS2024](https://ieeexplore.ieee.org/document/10781418) |APE |Visual-language model, semi-supervised learning, foundation model|WHU-CD, LEVIR-CD| ![Last Commit](https://img.shields.io/github/last-commit/likyoo/SemiCD-VL.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/likyoo/SemiCD-VL?style=social) |\n|2024|[ChangeCLIP](https://github.com/dyzy41/ChangeCLIP) | ChangeCLIP: Remote sensing change detection with multimodal vision-language representation learning | [ISPRS P\u0026RS 2024](https://www.sciencedirect.com/science/article/pii/S0924271624000042) |CLIP| Multimodal, vision-Language Representation Learning |WHU-CD, CDD, LEVIR-CD, LEVIR-CD+, and SYSU-CD| ![Last Commit](https://img.shields.io/github/last-commit/dyzy41/ChangeCLIP.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/dyzy41/ChangeCLIP?style=social) |\n| 2023 | [BAN](https://github.com/likyoo/BAN) | A New Learning Paradigm for Foundation Model-Based Remote-Sensing Change Detection | [TGRS2024](https://ieeexplore.ieee.org/abstract/document/10438490/) |CLIP| Foundation Model, visual tuning | WHU-CD, LEVIR-CD, S2Looking, Landsat-SCD, and BANDON| ![Last Commit](https://img.shields.io/github/last-commit/likyoo/BAN.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/likyoo/BAN?style=social) |\n| 2023 | [SAM-CD](https://github.com/ggsDing/SAM-CD) | Adapting Segment Anything Model for Change Detection in VHR Remote Sensing Images | [TGRS2024](https://ieeexplore.ieee.org/document/10443350)| SAM |Vision foundation models | WHU-CD, LEVIR-CD, CLCD, S2Looking| ![Last Commit](https://img.shields.io/github/last-commit/ggsDing/SAM-CD.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/ggsDing/SAM-CD?style=social) |\n\n\n### Diffusion Models and GANs\n\n| Year | Abbreviation | Title | Publication | Keywords | Experiment Datasets |Other      |\n| :--- | :--- | :--- | :--- | :--- | :--- |:--- |\n|2025|[CT2Net](https://github.com/Jiahuiqu/CT2Net)|Cycle Translation-Based Collaborative Training for Hyperspectral-RGB Multimodal Change Detection|[TPAMI2025](https://ieeexplore.ieee.org/document/11164958)|CycleGAN, Hyperspectral-RGB image, multimodal change detection, image translation, collaborative training|Bay Area (HSI-RGB), Santa Barbara (HSI-RGB), Hermiston (HSI-RGB), XDU-Liyukou (HSI-RGB)|![Last Commit](https://img.shields.io/github/last-commit/Jiahuiqu/CT2Net.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/Jiahuiqu/CT2Net?style=social)|\n|2025|[NeDS](https://github.com/Z-Zheng/pytorch-change-models)|Neural disaster simulation for transferable building damage assessment|[RSE2025](https://www.sciencedirect.com/science/article/pii/S0034425725003839?dgcid=rss_sd_all)|Synthetic data fine-tuning, deep generative models, conditional latent diffusion model|xBD, Los Angeles Wildfire (2025), and Nigeria Flooding (2025)|![Last Commit](https://img.shields.io/github/last-commit/Z-Zheng/pytorch-change-models.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/Z-Zheng/pytorch-change-models?style=social)|\n|2025|[BDGF](https://github.com/HaoLiu-XDU/BDGF)|Balanced Diffusion-Guided Fusion for Multimodal Remote Sensing Classification|[arXiv2025](https://arxiv.org/abs/2509.23310)|Denoising diffusion probabilistic models, adaptive modality masking strategy,mutual learning strategy|Berlin dataset (HSI+SAR), Augsburg dataset (HSI+SAR), Yellow River Estuary dataset (HSI+SAR), LCZ HK dataset (MSI+SAR)|![Last Commit](https://img.shields.io/github/last-commit/HaoLiu-XDU/BDGF.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/HaoLiu-XDU/BDGF?style=social)|\n|2025|[RS-NormGAN](https://github.com/lixinghua5540/RS-NormGAN)|RS-NormGAN: Enhancing change detection of multi-temporal optical remote sensing images through effective radiometric normalization|ISPRS P\u0026RS 2025|Deep style transfer; Domain adaptation; GAN; Multi-temporal radiometric normalization|GESD, SHCD|![Last Commit](https://img.shields.io/github/last-commit/lixinghua5540/RS-NormGAN.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/lixinghua5540/RS-NormGAN?style=social)|\n|2024|[UP-Diff](https://github.com/zeyuwang-zju/UP-Diff)|UP-Diff: Latent Diffusion Model for Remote Sensing Urban Prediction|[GRSL2024](https://ieeexplore.ieee.org/document/10807291)|Cross-attention, latent diffusion model, urban planning|LEVIR-CD, SYSU-CD|![Last Commit](https://img.shields.io/github/last-commit/zeyuwang-zju/UP-Diff.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/zeyuwang-zju/UP-Diff?style=social)|\n|2024|[ChangeDiff](https://github.com/DZhaoXd/ChangeDiff)|ChangeDiff: A Multi-Temporal Change Detection Data Generator with Flexible Text Prompts via Diffusion Model|[AAAI2025](https://ojs.aaai.org/index.php/AAAI/article/view/33058)|Diffusion models, text-to-layout model, multi-class distribution-guided text prompts|SECOND, Landsat-SCD, and HRSCD|![Last Commit](https://img.shields.io/github/last-commit/DZhaoXd/ChangeDiff.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/DZhaoXd/ChangeDiff?style=social)|\n| 2024 | [Changen2](https://github.com/Z-Zheng/pytorch-change-models/tree/main/torchange/models/changen2) | Changen2: Multi-Temporal Remote Sensing Generative Change Foundation Model | [TPAMI2024](https://ieeexplore.ieee.org/document/10713915/) | Synthetic data pre-training, generative model, foundation model | WHU-CD, xBD, LEVIR-CD, S2Looking, SECOND| ![Last Commit](https://img.shields.io/github/last-commit/Z-Zheng/pytorch-change-models.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/Z-Zheng/pytorch-change-models?style=social) |\n| 2023 | [Changen](https://github.com/Z-Zheng/Changen) | Scalable Multi-Temporal Remote Sensing Change Data Generation via Simulating Stochastic Change Process | [ICCV2023](https://openaccess.thecvf.com/content/ICCV2023/papers/Zheng_Scalable_Multi-Temporal_Remote_Sensing_Change_Data_Generation_via_Simulating_Stochastic_ICCV_2023_paper.pdf) | Deep generative model, change event simulation, semantic change synthesis | WHU-CD, LEVIR-CD, S2Looking| ![Last Commit](https://img.shields.io/github/last-commit/Z-Zheng/Changen.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/Z-Zheng/Changen?style=social) |\n| 2022 | [DDPM-CD](https://github.com/wgcban/ddpm-cd) | DDPM-CD: Denoising Diffusion Probabilistic Models as Feature Extractors for Remote Sensing Change Detection| [WACV2025](https://openaccess.thecvf.com/content/WACV2025/papers/Bandara_DDPM-CD_Denoising_Diffusion_Probabilistic_Models_as_Feature_Extractors_for_Remote_WACV_2025_paper.pdf) | Image synthesis, Denoising Diffusion Probabilistic Models | WHU-CD, CDD, DSIFN-CD, and LEVIR-CD | ![Last Commit](https://img.shields.io/github/last-commit/wgcban/ddpm-cd.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/wgcban/ddpm-cd?style=social) |\n\n\n### Transformers\n\n| Year | Abbreviation | Title | Publication | Keywords | Experiment Datasets |Other|\n| :--- | :--- | :--- | :--- | :--- | :--- |:--- |\n|2025|[BTC](https://github.com/blaz-r/BTC-change-detection)|Be the Change You Want to See: Revisiting Remote Sensing Change Detection Practices|[TGRS2025](https://ieeexplore.ieee.org/document/11063303)|Change detection, method optimization, remote sensing, supervised learning|SYSU-CD, LEVIR-CD, EGY-BCD, GVLM-CD, CLCD, OSCD|![Last Commit](https://img.shields.io/github/last-commit/blaz-r/BTC-change-detection.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/blaz-r/BTC-change-detection?style=social)|\n|2025|[CMNet](https://github.com/Jscript10/CMNet)|CMNet: A CNN–Mamba Network for Change Detection With Similarity Orientation and Difference Perception|[TGRS2025](https://ieeexplore.ieee.org/document/11208164/)|Difference perception, similarity orientation, CNN–Mamba|DSIFN-CD, LEVIR-CD, SYSU-CD|![Last Commit](https://img.shields.io/github/last-commit/Jscript10/CMNet.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/Jscript10/CMNet?style=social)|\n|2025|[MBUKG_DCKRL_CD](https://figshare.com/articles/online_resource/MBUKG_DCKRL_CD/27897873)|An urban change detection method based on multimodal data and knowledge graph technology|[IJDE2025](https://www.tandfonline.com/doi/full/10.1080/17538947.2025.2564902?af=R)|Urban change detection, multimodal knowledge graph, multi-source data representation learning, dual cross-attention mechanism|A comprehensive dataset comprising VHR images, POI data, and SVI|-|\n|2025|[S-cCDNet](https://github.com/Shelly-H/S-cCDNet)|Semantic-centric change detection framework: considering spatiotemporal heterogeneity and spatiotemporal correlation of land cover|[IJDE2025](https://www.tandfonline.com/doi/full/10.1080/17538947.2025.2569406?af=R)|Multi-task learning, prototype representation, spatiotemporal heterogeneity, spatiotemporal correlation|SECOND, Landsat-SCD|![Last Commit](https://img.shields.io/github/last-commit/Shelly-H/S-cCDNet.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/Shelly-H/S-cCDNet?style=social)|\n|2025|[CPGNet](https://github.com/long123524/CPGNet)|Detecting semantic changes from VHR remote sensing images by integrating semantic correlations and change priors|[JAG2025](https://www.sciencedirect.com/science/article/pii/S1569843225005631?dgcid=rss_sd_all)|Semantic correlations; Multi-view; Change prior-guided network|JL1; Xiamen (XM) cropland non-agriculturalization dataset; SECOND|![Last Commit](https://img.shields.io/github/last-commit/long123524/CPGNet.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/long123524/CPGNet?style=social)|\n|2025|[FoBa](https://github.com/zmoka-zht/FoBa)|FoBa: A Foreground-Background co-Guided Method and New Benchmark for Remote Sensing Semantic Change Detection|[arXiv2025](https://arxiv.org/abs/2509.15788)| foreground background co-guided, bi-temporal interaction, mamba, new benchmark|SECOND, JL1, and the proposed LevirSCD|![Last Commit](https://img.shields.io/github/last-commit/zmoka-zht/FoBa.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/zmoka-zht/FoBa?style=social)|\n|2025|[GSTM-SCD](https://github.com/liuxuanguang/GSTM-SCD)|GSTM-SCD: Graph-enhanced spatio-temporal state space model for semantic change detection in multi-temporal remote sensing images|[ISPRS P\u0026RS 2025](https://www.sciencedirect.com/science/article/pii/S0924271625003557?dgcid=rss_sd_all)|State space model, Graph optimization, Spatio-temporal modeling|SECOND, Landsat-SCD, WUSU and DynamicEarthNet|![Last Commit](https://img.shields.io/github/last-commit/liuxuanguang/GSTM-SCD.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/liuxuanguang/GSTM-SCD?style=social)|\n|2025|[Change3D](https://github.com/zhuduowang/Change3D)|Change3D: Revisiting Change Detection and Captioning from A Video Modeling Perspective| [CVPR2025](https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_Change3D_Revisiting_Change_Detection_and_Captioning_from_A_Video_Modeling_CVPR_2025_paper.html)|Perception Feature Extraction, Change Decoder, Caption Decoder|WHU-CD, HRSCD, xBD, LEVIR-CD, CLCD, SECOND, LEVIR-CC, and DUBAI-CC|![Last Commit](https://img.shields.io/github/last-commit/zhuduowang/Change3D.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/zhuduowang/Change3D?style=social)|\n| 2025 |[UA-BCD](https://github.com/Henryjiepanli/UA-BCD)| Overcoming the uncertainty challenges in detecting building changes from remote sensing images | [ISPRS P\u0026RS 2025](https://www.sciencedirect.com/science/article/pii/S092427162400426X) | Uncertainty-related theory, Building change detection mapping, Siamese pyramid vision transformer|CDD, WHU-CD, GZ-CD, LEVIR-CD, SYSU-CD| ![Last Commit](https://img.shields.io/github/last-commit/Henryjiepanli/UA-BCD.svg?style=flat\u0026logo=github\u0026label=Last%20Commit) ![GitHub stars](https://img.shields.io/github/stars/Henryjiepanli/UA-BCD?style=social)|\n|2025|[SMGNet](https://github.com/long123524/SMGNet)|SMGNet: A Semantic Map-Guided Multitask Neural Network for Remote Sensing Image Semantic Change Detection|[GRSL2025](https://ieeexplore.ieee.org/document/11023838)|Historical semantic information, pseudochanges, semantic map-guided network, underdetection|HRSCD|![Last Commit](https://img.shields.io/github/last-commit/long123524/SMGNet.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/long123524/SMGNet?style=social)|\n|2024|[HGINet](https://github.com/long123524/HGINet-torch)|Semantic change detection using a hierarchical semantic graph interaction network from high-resolution remote sensing images|[ISPRS P\u0026RS 2024](https://www.sciencedirect.com/science/article/pii/S0924271624001709)|Hierarchical semantic graph interaction network; Temporal correlations; Semantic difference interaction|SECOND, HRSCD, Fuzhou, and Xiamen|![Last Commit](https://img.shields.io/github/last-commit/long123524/HGINet-torch.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/long123524/HGINet-torch?style=social)|\n| 2024 |STCA| Towards transferable building damage assessment via unsupervised single-temporal change adaptation | [RSE2024](https://www.sciencedirect.com/science/article/pii/S0034425724004425?dgcid=rss_sd_all) | Unsupervised adaptation, single-temporal learning, semantic change detection | xBD, Turkey–Syria earthquake (2023), Kalehe DRC flooding (2023), Maui Hawaii fire (2023) | - |\n| 2024 | [ChangeSparse](https://github.com/Z-Zheng/pytorch-change-models/blob/main/torchange/models/changesparse.py) | Unifying Remote Sensing Change Detection via Deep Probabilistic Change Models: from Principles, Models to Applications | [ISPRS P\u0026RS 2024](https://www.sciencedirect.com/science/article/pii/S0924271624002624) | Probabilistic change model, sparsity of change, sparse change transformer | CDD, S2Looking, California Flood dataset, xBD, SECOND, DynamicEarthNet | ![Last Commit](https://img.shields.io/github/last-commit/Z-Zheng/pytorch-change-models.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/Z-Zheng/pytorch-change-models?style=social) |\n| 2024 | [ChangeStar2](https://github.com/Z-Zheng/pytorch-change-models/blob/main/torchange/models/changestar2.py) | Single-Temporal Supervised Learning for Universal Remote Sensing Change Detection | [IJCV2024](https://link.springer.com/article/10.1007/s11263-024-02141-4) | Universal change detection, single-temporal supervised learning |WHU-CD, CDD, xBD, LEVIR-CD, S2Looking, SpaceNet8, DynamicEarthNet, SECOND| ![Last Commit](https://img.shields.io/github/last-commit/Z-Zheng/pytorch-change-models.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/Z-Zheng/pytorch-change-models?style=social) |\n| 2024 | [BiFA](https://github.com/zmoka-zht/BiFA) | BiFA: Remote Sensing Image Change Detection With Bitemporal Feature Alignment | [TGRS2024](https://ieeexplore.ieee.org/document/10471555) | Bitemporal interaction, feature alignment, flow field|WHU-CD, LEVIR-CD, LEVIR-CD+, SYSU-CD, DSIFN-CD, and CLCD| ![Last Commit](https://img.shields.io/github/last-commit/zmoka-zht/BiFA.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/zmoka-zht/BiFA?style=social)|\n| 2024 | [CDMamba](https://github.com/zmoka-zht/CDMamba) | CDMamba: Incorporating Local Clues Into Mamba for Remote Sensing Image Binary Change Detection | [TGRS2025](https://ieeexplore.ieee.org/document/10902569) | Mamba, bitemporal interaction, state space model|WHU-CD, CDD, LEVIR-CD, LEVIR-CD+, and CLCD|![Last Commit](https://img.shields.io/github/last-commit/zmoka-zht/CDMamba.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/zmoka-zht/CDMamba?style=social) |\n| 2024 | [CDMask](https://github.com/xwmaxwma/rschange) | Rethinking Remote Sensing Change Detection With A Mask View | [arXiv2024](https://arxiv.org/abs/2406.15320) |Mask view, mask-level Classification, MaskFormer|WHU-CD, LEVIR-CD, SYSU-CD, DSIFN-CD, and CLCD|![Last Commit](https://img.shields.io/github/last-commit/xwmaxwma/rschange.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/xwmaxwma/rschange?style=social)|\n| 2024 | [ChangeMamba](https://github.com/ChenHongruixuan/ChangeMamba) | ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space Model | [TGRS2024](https://ieeexplore.ieee.org/document/10565926) | Mamba, spatiotemporal relationship, state space model|WHU-CD, xBD, SECOND, LEVIR-CD+, and SYSU-CD|![Last Commit](https://img.shields.io/github/last-commit/ChenHongruixuan/ChangeMamba.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/ChenHongruixuan/ChangeMamba?style=social) |\n| 2024 | [MaskCD](https://github.com/AI4RS/MaskCD) | MaskCD: A Remote Sensing Change Detection Network Based on Mask Classification | [TGRS2024](https://ieeexplore.ieee.org/document/10587034) | Deformable attention, mask classification, masked cross-attention|LEVIR-CD, CLCD, SYSU-CD, EGY-BCD, and GVLM-CD|![Last Commit](https://img.shields.io/github/last-commit/AI4RS/MaskCD.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/AI4RS/MaskCD?style=social) |\n| 2024 | [M-CD](https://github.com/JayParanjape/M-CD) | A Mamba-based Siamese Network for Remote Sensing Change Detection | [arXiv2024](https://arxiv.org/abs/2407.06839) | Mamba, state space model, difference Module| WHU-CD, CDD, DSIFN-CD, and LEVIR-CD|![Last Commit](https://img.shields.io/github/last-commit/JayParanjape/M-CD.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/JayParanjape/M-CD?style=social) |\n| 2024 | [ScratchFormer](https://github.com/mustansarfiaz/ScratchFormer) | Remote Sensing Change Detection With Transformers Trained From Scratch | [TGRS2024](https://ieeexplore.ieee.org/document/10489990) | Trained from scratch, shuffled sparse-attention operation, change-enhanced feature fusion, | WHU-CD, OSCD, CDD, DSIFN-CD, and LEVIR-CD|![Last Commit](https://img.shields.io/github/last-commit/mustansarfiaz/ScratchFormer.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/mustansarfiaz/ScratchFormer?style=social) |\n| 2024 | [SitsSCD](https://github.com/ElliotVincent/SitsSCD) |Satellite Image Time Series Semantic Change Detection: Novel Architecture and Analysis of Domain Shift| [arXiv2024](https://arxiv.org/abs/2407.07616)| Temporal attention, Temporal shift, Spatial shift| DynamicEarthNet, MUDS|![Last Commit](https://img.shields.io/github/last-commit/ElliotVincent/SitsSCD.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/ElliotVincent/SitsSCD?style=social) |\n| 2023 | [3DCD](https://github.com/VMarsocci/3DCD) | Inferring 3D change detection from bitemporal optical images | [ISPRS P\u0026RS 2023](https://www.sciencedirect.com/science/article/pii/S0924271622003240) | 3D Change Detection, Elevation change detection | 3DCD | ![Last Commit](https://img.shields.io/github/last-commit/VMarsocci/3DCD.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/VMarsocci/3DCD?style=social) |\n| 2023 | [Siamese KPConv](https://github.com/IdeGelis/torch-points3d-SiameseKPConv) | Siamese KPConv: 3D multiple change detection from raw point clouds using deep learning | [ISPRS P\u0026RS 2023](https://www.sciencedirect.com/science/article/pii/S0924271623000394?via%3Dihub) | 3D Change Detection, Siamese network, 3D Kernel Point Convolution | Urb3DCD–V2, AHN-CD, Change3D | ![Last Commit](https://img.shields.io/github/last-commit/IdeGelis/torch-points3d-SiameseKPConv.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/IdeGelis/torch-points3d-SiameseKPConv?style=social) |\n| 2023 | [MapFormer](https://github.com/mxbh/mapformer) | MapFormer: Boosting Change Detection by Using Pre-change Information | [ICCV2023](https://openaccess.thecvf.com/content/ICCV2023/papers/Bernhard_MapFormer_Boosting_Change_Detection_by_Using_Pre-change_Information_ICCV_2023_paper.pdf) | Conditional Change Detection, multi-modal feature fusion, cross-modal contrastive loss|HRSCD, DynamicEarthNet   | ![Last Commit](https://img.shields.io/github/last-commit/mxbh/mapformer.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/mxbh/mapformer?style=social) |\n| 2023 | [CACo](https://github.com/utkarshmall13/caco) | Change-Aware Sampling and Contrastive Learning for Satellite Images | [CVPR2023](https://openaccess.thecvf.com/content/CVPR2023/papers/Mall_Change-Aware_Sampling_and_Contrastive_Learning_for_Satellite_Images_CVPR_2023_paper.pdf) | Self-supervised learning, Change-Aware Contrastive Loss| OSCD, DynamicEarthNet, EuroSat, and BigEarthNet| ![Last Commit](https://img.shields.io/github/last-commit/utkarshmall13/caco.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/utkarshmall13/caco?style=social) |\n| 2023 | [Self-Pair](https://github.com/seominseok0429/Self-Pair-for-Change-Detection) | Self-Pair: Synthesizing Changes from Single Source for Object Change Detection in Remote Sensing Imagery | [WACV2023](https://openaccess.thecvf.com/content/WACV2023/papers/Seo_Self-Pair_Synthesizing_Changes_From_Single_Source_for_Object_Change_Detection_WACV_2023_paper.pdf) | Synthetic data, single-temporal supervision, visual similarity in unchanged area | WHU-CD, SpaceNet2, xBD, LEVIR-CD | ![Last Commit](https://img.shields.io/github/last-commit/seominseok0429/Self-Pair-for-Change-Detection.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/seominseok0429/Self-Pair-for-Change-Detection?style=social) |\n| 2022 | [Changer](https://github.com/likyoo/open-cd/tree/main/configs/changer) | Changer: Feature Interaction is What You Need for Change Detection | [TGRS2023](https://ieeexplore.ieee.org/document/10129139) | Feature Interaction | LEVIR-CD, S2Looking | ![Last Commit](https://img.shields.io/github/last-commit/likyoo/open-cd.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/likyoo/open-cd?style=social) |\n| 2022 | [ChangeMask](https://github.com/Z-Zheng/pytorch-change-models/blob/main/torchange/models/changemask.py) | ChangeMask: Deep Multi-task Encoder-Transformer-Decoder Architecture for Semantic Change Detection | [ISPRS P\u0026RS 2022](https://www.sciencedirect.com/science/article/pii/S0924271621002835) | Multi-task learning, temporal symmetry, multi-temporal|SECOND, Hi-UCD| ![Last Commit](https://img.shields.io/github/last-commit/Z-Zheng/pytorch-change-models.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/Z-Zheng/pytorch-change-models?style=social) |\n| 2022 | [FHD](https://github.com/ZSVOS/FHD) | Feature Hierarchical Differentiation for Remote Sensing Image Change Detection | [GRSL2022](https://ieeexplore.ieee.org/document/9837915) | Hierarchical differentiation, time-specific features| DSIFN, LEVIR-CD, LEVIR-CD+, S2Looking | ![Last Commit](https://img.shields.io/github/last-commit/ZSVOS/FHD.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/ZSVOS/FHD?style=social) |\n| 2022 | [SST-Former](https://github.com/yanhengwang-heu/IEEE_TGRS_SSTFormer) | Spectral–spatial–temporal transformers for hyperspectral image change detection | [TGRS2022](https://ieeexplore.ieee.org/abstract/document/9870837) | Hyperspectral, cross-attention, self-attention |Farmland CD dataset, Barbara CD dataset, and Bay Area CD dataset  | ![Last Commit](https://img.shields.io/github/last-commit/yanhengwang-heu/IEEE_TGRS_SSTFormer.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/yanhengwang-heu/IEEE_TGRS_SSTFormer?style=social) |\n| 2022 | [CDViT](https://github.com/shinianzhihou/ChangeDetection) | A Divided Spatial and Temporal Context Network for Remote Sensing Change Detection | [JSTARS2022](https://ieeexplore.ieee.org/document/9779962) | Self-attention, spatial-temporal transformer | WHU-CD, LEVIR-CD | ![Last Commit](https://img.shields.io/github/last-commit/shinianzhihou/ChangeDetection.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/shinianzhihou/ChangeDetection?style=social) |\n| 2022 | [ChangeFormer](https://github.com/wgcban/ChangeFormer) | A Transformer-Based Siamese Network for Change Detection | [IGARSS2022](https://ieeexplore.ieee.org/document/9883686) | Transformer Siamese network, attention mechanism| DSIFN-CD, and LEVIR-CD| ![Last Commit](https://img.shields.io/github/last-commit/wgcban/ChangeFormer.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/wgcban/ChangeFormer?style=social) |\n| 2021 | [BIT](https://github.com/justchenhao/BIT_CD) | Remote Sensing Image Change Detection with Transformers | [TGRS2021](https://ieeexplore.ieee.org/document/9491802) | Transformer | WHU-CD, DSIFN-CD, LEVIR-CD | ![Last Commit](https://img.shields.io/github/last-commit/justchenhao/BIT_CD.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/justchenhao/BIT_CD?style=social) |\n\n\n### CNNs\n\n| Year | Abbreviation | Title | Publication | Keywords | Experiment Datasets |Other|\n| :--- | :--- | :--- | :--- | :--- | :--- |:--- |\n|2025|[RACDNet](https://github.com/LYT-max/RACDNet)|Towards resolution-arbitrary remote sensing change detection with Spatial-frequency dual domain learning|[ISPRS P\u0026RS 2025](https://www.sciencedirect.com/science/article/pii/S0924271625004113?dgcid=rss_sd_all)|Resolution-arbitrary change detection; Gradient prior; Dual-domain learning|WHU-CD, LEVIR-CD, SYSU-CD, and Google dataset|![Last Commit](https://img.shields.io/github/last-commit/LYT-max/RACDNet.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/LYT-max/RACDNet?style=social)|\n|2025|[RIEM](https://github.com/yulisun/RIEM)|Detecting changes without comparing images: Rules induced change detection in heterogeneous remote sensing images|[ISPRS P\u0026RS 2025](https://www.sciencedirect.com/science/article/pii/S0924271625003612?dgcid=rss_sd_all)|Heterogeneous data, Multimodal, Energy based model|Multi-source data|![Last Commit](https://img.shields.io/github/last-commit/yulisun/RIEM.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/yulisun/RIEM?style=social)|\n|2025|[Semantic-TemporalNet](https://github.com/CUG-BEODL/STN)|Semantic-TemporalNet: A Novel Urban Block Change Detection Method Based on Semantic Coherence Analysis|[TGRS2025](https://ieeexplore.ieee.org/document/11172373)|Sentinel-2, time-series semantic coherence, urban renewal|Changsha and Wuhan Sentinel-2 imagery|![Last Commit](https://img.shields.io/github/last-commit/CUG-BEODL/STN.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/CUG-BEODL/STN?style=social)|\n|2025|[TripleS](https://github.com/StephenApX/MTL-TripleS)|TripleS: Mitigating multi-task learning conflicts for semantic change detection in high-resolution remote sensing imagery|[ISPRS P\u0026RS 2025](https://www.sciencedirect.com/science/article/pii/S0924271625003776?dgcid=rss_sd_all)|Multi-task learning, Land-cover and land-use|HRSCD, SC-SCD7, CC-SCD5|![Last Commit](https://img.shields.io/github/last-commit/StephenApX/MTL-TripleS.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/StephenApX/MTL-TripleS?style=social)|\n| 2025 | CDFNet | Cross-scenario damaged building extraction network: Methodology, application, and efficiency using single-temporal HRRS imagery | [ISPRS P\u0026RS2025](https://www.sciencedirect.com/science/article/pii/S0924271625002540?dgcid=rss_sd_all) | Building damage extraction, cross-scenario, single-temporal, feature decomposition | Post-disaster damaged building datasets of Yunnan and Harvey,  An auxiliary dataset (two earthquake-prone, one flood-prone, four non-disaster areas),  An application testing dataset (eight heterogeneous regions spanning volcano, earthquake, tsunami, wildfire, and hurricane scenarios) |-|\n|2025|[PRO-HRSCD](https://github.com/sdust-mmlab/PRO-HRSCD)|Rethinking Semantic Change Detection From a Semantic Alignment Perspective|[TGRS2025](https://ieeexplore.ieee.org/document/11162709/)|Feature space alignment, multitask learning, prototype learning|SECOND, and Landsat-SCD|![Last Commit](https://img.shields.io/github/last-commit/sdust-mmlab/PRO-HRSCD.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/sdust-mmlab/PRO-HRSCD?style=social)|\n|2025|H-FIENet|Flood inundation monitoring using multi-source satellite imagery: a knowledge transfer strategy for heterogeneous image change detection|[RSE2024](https://www.sciencedirect.com/science/article/pii/S0034425724003997)|Flood mapping, Multi-source images, Cross-task transform|Gaofen-2, Gaofen-3, Sentinel-1, and Sentinel-2|-|\n|2024|[STMNet](https://github.com/Zhoutya/ChangeDetection-STMNet)|STMNet: Single-Temporal Mask-Based Network for Self-Supervised Hyperspectral Change Detection|[TGRS2024](https://ieeexplore.ieee.org/document/10817647)|hyperspectral image, multiscale feature, single temporal, mask|Farmland dataset, Hermiston dataset, Bay dataset|![Last Commit](https://img.shields.io/github/last-commit/Zhoutya/ChangeDetection-STMNet.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/Zhoutya/ChangeDetection-STMNet?style=social)|\n|2024|[ClearSCD](https://github.com/tangkai-RS/ClearSCD)|The ClearSCD model: Comprehensively leveraging semantics and change relationships for semantic change detection in high spatial resolution remote sensing imagery|[ISPRS P\u0026RS 2024](https://www.sciencedirect.com/science/article/pii/S0924271624001734)|Multi-task learning, contrastive learning, change vector analysis|Hi-UCD, LsSCD|![Last Commit](https://img.shields.io/github/last-commit/tangkai-RS/ClearSCD.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/tangkai-RS/ClearSCD?style=social) |\n| 2024 | [SSLChange](https://github.com/MarsZhaoYT/SSLChange)| SSLChange: A Self-Supervised Change Detection Framework Based on Domain Adaptation| [TGRS2024](https://ieeexplore.ieee.org/document/10741199)| Domain adaption, hierarchical features, image contrastive learning|CDD, LEVIR-CD|![Last Commit](https://img.shields.io/github/last-commit/MarsZhaoYT/SSLChange.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/MarsZhaoYT/SSLChange?style=social) |\n|2024|[U-Net, U-Net SiamDiff, and U-Net SiamConc](https://github.com/isaaccorley/a-change-detection-reality-check) | A Change Detection Reality Check | [ICLR2024W](https://arxiv.org/abs/2402.06994) | Reality Check, Benchmarking |WHU-CD, LEVIR-CD| ![Last Commit](https://img.shields.io/github/last-commit/isaaccorley/a-change-detection-reality-check.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/isaaccorley/a-change-detection-reality-check?style=social) |\n|2024|CCNet| Harmony in diversity: Content cleansing change detection framework for very-high-resolution remote-sensing images|[ISPRS P\u0026RS 2024](https://www.sciencedirect.com/science/article/pii/S092427162400340X)|Feature disentanglement, Content cleansing, Image restoration|CDD, LEVIR-CD, xBD, SECOND, SYSU-CD, Multi-temporal xBD|-|\n| 2023 | [I3PE](https://github.com/ChenHongruixuan/I3PE) | Exchange means change: An unsupervised single-temporal change detection framework based on intra-and inter-image patch exchange | [ISPRS P\u0026RS 2023](https://www.sciencedirect.com/science/article/pii/S092427162300309X) | Single-temporal change detection, image patch exchange, adaptive clustering | SYSU-CD, SECOND, Wuhan dataset| ![Last Commit](https://img.shields.io/github/last-commit/ChenHongruixuan/I3PE.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/ChenHongruixuan/I3PE?style=social) |\n| 2023 | [A2Net](https://github.com/guanyuezhen/A2Net) | Lightweight Remote Sensing Change Detection With Progressive Feature Aggregation and Supervised Attention | [TGRS2023](https://ieeexplore.ieee.org/document/10034814) | Lightweight, progressive feature aggregation, supervised Attention |WHU-CD, LEVIR-CD and SYSU-CD|![Last Commit](https://img.shields.io/github/last-commit/guanyuezhen/A2Net.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/guanyuezhen/A2Net?style=social) |\n| 2023 | [DMINet](https://github.com/ZhengJianwei2/DMINet) | Change Detection on Remote Sensing Images Using Dual-Branch Multilevel Intertemporal Network | [TGRS2023](https://ieeexplore.ieee.org/document/10034787) | Dual-branch difference acquisition, intertemporal joint-attention, multilevel aggregation|WHU-CD, GZ-CD, LEVIR-CD, and SYSU-CD|![Last Commit](https://img.shields.io/github/last-commit/ZhengJianwei2/DMINet.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/ZhengJianwei2/DMINet?style=social) |\n| 2023 | [AFCF3D-Net](https://github.com/wm-Githuber/AFCF3D-Net) | Adjacent-level feature cross-fusion with 3D CNN for remote sensing image change detection | [TGRS2023](https://ieeexplore.ieee.org/document/10221754) | 3D CNN, feature cross-fusion, full-scale connection|WHU-CD, LEVIR-CD, SYSU-CD| ![Last Commit](https://img.shields.io/github/last-commit/wm-Githuber/AFCF3D-Net.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/wm-Githuber/AFCF3D-Net?style=social) |\n| 2023 | [LightCDNet](https://github.com/NightSongs/LightCDNet) | LightCDNet: Lightweight Change Detection Network Based on VHR Images | [GRSL2023](https://ieeexplore.ieee.org/document/10214556) | Early fusion, lightweight, deep supervised fusion|LEVIR-CD| ![Last Commit](https://img.shields.io/github/last-commit/NightSongs/LightCDNet.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/NightSongs/LightCDNet?style=social) |\n| 2023 | [USSFC-Net](https://github.com/SUST-reynole/USSFC-Net) | Ultralightweight Spatial–Spectral Feature Cooperation Network for Change Detection in Remote Sensing Images | [TGRS2023](https://ieeexplore.ieee.org/document/10081023) | Ultralightweight, multiscale feature extraction, spatial–spectral feature cooperation| CDD, DSIFN-CD, LEVIR-CD | ![Last Commit](https://img.shields.io/github/last-commit/SUST-reynole/USSFC-Net.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/SUST-reynole/USSFC-Net?style=social) |\n| 2023 | [SAR-CD](https://github.com/janne-alatalo/sar-change-detection) | Improved Difference Images for Change Detection Classifiers in SAR Imagery Using Deep Learning | [TGRS2023](https://ieeexplore.ieee.org/document/10286479) |Mapping transformation function, SAR, U-Net| SCDD | ![Last Commit](https://img.shields.io/github/last-commit/janne-alatalo/sar-change-detection.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/janne-alatalo/sar-change-detection?style=social) |\n| 2022 | [RDPNet](https://github.com/Chnja/RDPNet) | RDP-Net: Region detail preserving network for change detection | [TGRS2022](https://ieeexplore.ieee.org/document/9970750) | Training strategy, edge loss, lightweight backbone | CDD,LEVIR-CD | ![Last Commit](https://img.shields.io/github/last-commit/Chnja/RDPNet.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/Chnja/RDPNet?style=social) |\n| 2022 | [FFCTL](https://github.com/lauraset/FFCTL) | A full-level fused cross-task transfer learning method for building change detection using noise-robust pretrained networks on crowdsourced labels | [RSE2022](https://www.sciencedirect.com/science/article/pii/S0034425722004771) | Transfer learning, crowdsourced label,pseudo label |ZY-3 building and change detection dataset  | ![Last Commit](https://img.shields.io/github/last-commit/lauraset/FFCTL.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/lauraset/FFCTL?style=social) |\n| 2022 | [SaDL_CD](https://github.com/justchenhao/SaDL_CD) | Semantic-aware Dense Representation Learning for Remote Sensing Image Change Detection | [TGRS2022](https://github.com/justchenhao/SaDL_CD) | Self-supervised learning, semantic-aware representation learning| WHU-CD, GZ-CD, LEVIR-CD | ![Last Commit](https://img.shields.io/github/last-commit/justchenhao/SaDL_CD.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/justchenhao/SaDL_CD?style=social) |\n| 2022 | [TinyCD](https://github.com/AndreaCodegoni/Tiny_model_4_CD) | TINYCD: A (Not So) Deep Learning Model For Change Detection | [Neural Comput \u0026 Applic 2022](https://github.com/AndreaCodegoni/Tiny_model_4_CD) | Lightweight, tiny Model, siamese U-Net architecture, feature interaction | WHU-CD, LEVIR-CD | ![Last Commit](https://img.shields.io/github/last-commit/AndreaCodegoni/Tiny_model_4_CD.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/AndreaCodegoni/Tiny_model_4_CD?style=social) |\n| 2022 | [SDACD](https://github.com/Perfect-You/SDACD) | An End-to-end Supervised Domain Adaptation Framework for Cross-Domain Change Detection | [PR2022](https://www.sciencedirect.com/science/article/pii/S003132032200440X) | Supervised Domain Adaptation, Image Adaptation, Feature Adaptation |CDD, and WHU-CD | ![Last Commit](https://img.shields.io/github/last-commit/Perfect-You/SDACD.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/Perfect-You/SDACD?style=social) |\n| 2022 | [Bi-SRNet](https://github.com/ggsDing/Bi-SRNet) | Bi-temporal semantic reasoning for the semantic change detection in HR remote sensing images | [TGRS2022](https://ieeexplore.ieee.org/document/9721305) | Triple-branch, semantic correlations| SECOND| ![Last Commit](https://img.shields.io/github/last-commit/ggsDing/Bi-SRNet.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/ggsDing/Bi-SRNet?style=social) |\n| 2022 | [SemiCD](https://github.com/wgcban/SemiCD) | Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Images | [arXiv2022](https://arxiv.org/abs/2204.08454) | Semi-supervised, Consistency Regularization | WHU-CD, LEVIR-CD | ![Last Commit](https://img.shields.io/github/last-commit/wgcban/SemiCD.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/wgcban/SemiCD?style=social) |\n| 2022 | [FCCDN](https://github.com/chenpan0615/FCCDN_pytorch) | FCCDN: Feature Constraint Network for VHR Image Change Detection | [ISPRS P\u0026RS 2022](https://www.sciencedirect.com/science/article/pii/S0924271622000636) | Self-supervised learning, non-local feature pyramid network, dual encoder-decoder backbone|WHU-CD, LEVIR-CD, SECOND| ![Last Commit](https://img.shields.io/github/last-commit/chenpan0615/FCCDN_pytorch.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/chenpan0615/FCCDN_pytorch?style=social) |\n| 2021 | [ChangeStar](https://github.com/Z-Zheng/ChangeStar) | Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery | [ICCV2021](https://openaccess.thecvf.com/content/ICCV2021/papers/Zheng_Change_Is_Everywhere_Single-Temporal_Supervised_Object_Change_Detection_in_Remote_ICCV_2021_paper.pdf) | Single-temporal supervision, temporal symmetry| xBD, SpaceNet2, WHU-CD, LEVIR-CD| ![Last Commit](https://img.shields.io/github/last-commit/Z-Zheng/ChangeStar.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/Z-Zheng/ChangeStar?style=social) |\n| 2021 | [ChangeOS](https://github.com/Z-Zheng/ChangeOS) | Building damage assessment for rapid disaster response with a deep object-based semantic change detection framework: from natural disasters to man-made disasters | [RSE2021](https://www.sciencedirect.com/science/article/pii/S0034425721003564) |Semantic change detection, disaster response, OBIA| xBD, The Beirut port explosion (2020), The Bata military barracks explosion (2021) | ![Last Commit](https://img.shields.io/github/last-commit/Z-Zheng/ChangeOS.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/Z-Zheng/ChangeOS?style=social) |\n| 2021 | [Optical-SAR-CD](https://gitlab.lrz.de/ai4eo/cd/-/tree/main/sarOpticalMultisensorTgrs2021) | Self-supervised multisensor change detection | [TGRS2021](https://ieeexplore.ieee.org/abstract/document/9538396) | Self-supervised, Multisensor | OSCD (Sentinel-2 and Sentinel-1) | - |\n| 2021 | [CEECNet](https://github.com/feevos/ceecnet) | Looking for change? Roll the Dice and demand Attention | [RS2021](https://www.mdpi.com/2072-4292/13/18/3707) | Dice similarity, attention module | WHU-CD, LEVIR-CD | ![Last Commit](https://img.shields.io/github/last-commit/feevos/ceecnet.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/feevos/ceecnet?style=social) |\n| 2021 | [ESCNet](https://github.com/Bobholamovic/ESCNet) | ESCNet: An End-to-End Superpixel-Enhanced Change Detection Network for Very-High-Resolution Remote Sensing Images | [TNNLS2021](https://ieeexplore.ieee.org/document/9474911) | Superpixel segmentation, adaptive superpixel merging | SZTAKI, CDD| ![Last Commit](https://img.shields.io/github/last-commit/Bobholamovic/ESCNet.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/Bobholamovic/ESCNet?style=social) |\n| 2021 | [SeCo](https://github.com/ElementAI/seasonal-contrast) | Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data | [ICCV2021](https://openaccess.thecvf.com/content/ICCV2021/html/Manas_Seasonal_Contrast_Unsupervised_Pre-Training_From_Uncurated_Remote_Sensing_Data_ICCV_2021_paper.html) | Self-supervised learning| BigEarthNet, EuroSAT, OSCD | ![Last Commit](https://img.shields.io/github/last-commit/ElementAI/seasonal-contrast.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/ElementAI/seasonal-contrast?style=social) |\n| 2021 | [SRCDNet](https://github.com/liumency/SRCDNet) | Super-resolution-based Change Detection Network with Stacked Attention Module for Images with Different Resolutions | [TGRS2021](https://ieeexplore.ieee.org/document/9472869) | Super-resolution, metric learning | BCDD, CDD, GZ-CD | ![Last Commit](https://img.shields.io/github/last-commit/liumency/SRCDNet.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/liumency/SRCDNet?style=social) |\n| 2021 | [IAug-CDNet](https://github.com/justchenhao/IAug_CDNet) | Adversarial Instance Augmentation for Building Change Detection in Remote Sensing Images | [TGRS2021](https://ieeexplore.ieee.org/abstract/document/9386248) | Adversarial instance augmentation, synthetic data| WHU-CD, LEVIR-CD | ![Last Commit](https://img.shields.io/github/last-commit/justchenhao/IAug_CDNet.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/justchenhao/IAug_CDNet?style=social) |\n| 2021 | [SNUNet-CD](https://github.com/likyoo/open-cd/) | SNUNet-CD: A Densely Connected Siamese Network for Change Detection of VHR Images | [GRSL2021](https://ieeexplore.ieee.org/document/9355573) | Fully convolutional siamese network | CDD | ![Last Commit](https://img.shields.io/github/last-commit/likyoo/open-cd.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/likyoo/open-cd?style=social) |\n| 2021 | [DDNet](https://github.com/summitgao/SAR_CD_DDNet) | Change Detection in Synthetic Aperture Radar Images Using a Dual-Domain Network | [GRSL2021](https://ieeexplore.ieee.org/abstract/document/9420150) | SAR, frequency domain | Ottawa dataset, Sulzberger dataset, Yellow River dataset| ![Last Commit](https://img.shields.io/github/last-commit/summitgao/SAR_CD_DDNet.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/summitgao/SAR_CD_DDNet?style=social) |\n| 2021 | [ACDA](https://github.com/meiqihu/ACDA) | Hyperspectral anomaly change detection based on autoencoder | [JSTARS2021](https://ieeexplore.ieee.org/document/9380336) | Hyperspectral, Anomaly Change Detection, Autoencoder | Viareggio 2013| ![Last Commit](https://img.shields.io/github/last-commit/meiqihu/ACDA.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/meiqihu/ACDA?style=social) |\n| 2018 | [FC-EF, FC-Siam-diff, FC-Siam-conc](https://github.com/rcdaudt/fully_convolutional_change_detection) | Fully convolutional siamese networks for change detection | [ICIP2018](https://ieeexplore.ieee.org/abstract/document/8451652) | Fully Convolutional Siamese Networks | SZTAKI, OSCD | ![Last Commit](https://img.shields.io/github/last-commit/rcdaudt/fully_convolutional_change_detection.svg?style=flat\u0026logo=github\u0026label=Last%20Commit)![GitHub stars](https://img.shields.io/github/stars/rcdaudt/fully_convolutional_change_detection?style=social) |\n\n\n## Traditional Methods\n\n| Year | Abbreviation | Title | Publication | Keywords | Experiment Datasets |\n| :--- | :--- | :--- | :--- | :--- | :--- |\n|2025|[PWTT](https://github.com/oballinger/PWTT)|Open access battle damage detection via Pixel-Wise T-Test on Sentinel-1 imagery|[RSE2025](https://www.sciencedirect.com/science/article/pii/S0034425725004298?dgcid=rss_sd_all)|Probabilistic change detection; Building damage assessment; Armed conflict|[UNOSAT damage annotation dataset](https://github.com/oballinger/PWTT)|\n|2021|[SiROC](https://github.com/lukaskondmann/SiROC)|Spatial Context Awareness for Unsupervised Change Detection in Optical Satellite Images|[TGRS2021](https://ieeexplore.ieee.org/document/9627707)|Multitemporal, optical images, unsupervised, urban analysis|OSCD, Beirut Harbor Explosion Dataset, Agriculture Dataset, Alpine Dataset|\n|2015|[CCDC](https://github.com/GERSL/CCDC)|Generating synthetic Landsat images based on all available Landsat data: Predicting Landsat surface reflectance at any given time|[RSE2015](https://www.sciencedirect.com/science/article/pii/S0034425715000590)|Synthetic, Landsat, Surface reflectance, Time series model| Six Landsat scenes at different places in the Conterminous United States|\n|2013|SFA|Slow Feature Analysis for Change Detection in Multispectral Imagery|[TGRS2013](https://ieeexplore.ieee.org/abstract/document/6553145)|Image transformation, slow feature analysis|Taizhou City ETM Data Set, Kunshan City ETM Data|\n|2010|CVAPS|Change Vector Analysis in Posterior Probability Space: A New Method for Land Cover Change Detection|[GRSL2010](https://ieeexplore.ieee.org/abstract/document/5597922)|CVA, land cover change, postclassification comparison (PCC), posterior probability space| Multitemporal Landsat Thematic Mapper (TM) data for Shunyi District, Beijing, China|\n|2009|PCA-Kmeans|Unsupervised Change Detection in Satellite Images Using Principal Component Analysis and k-Means Clustering|[GRSL2009](https://ieeexplore.ieee.org/abstract/document/5196726)|K-means clustering, multitemporal satellite images, optical images, principal component analysis (PCA)|The optical images of Lake Tahoe, Reno, Nevada|\n|2007|[IR-MAD](http://people.compute.dtu.dk/alan/software.html) | The Regularized Iteratively Reweighted Multivariate Alteration Detection | [TIP2007](https://ieeexplore.ieee.org/abstract/document/4060945) |Canonical correlation analysis (CCA), iteratively reweighted multivariate alteration detection (IR-MAD), MAD transformation, regularization or penalization|Partly Constructed Landsat TM Data, Northern Swede; SPOT HRV Data, Kiambu District, Kenya; Hymap Data, Lake Waging-Taching, Germany|\n|2003|ICVA|Land-Use/Land-Cover Change Detection Using Improved Change-Vector Analysis|[PERS2003](https://www.ingentaconnect.com/content/asprs/pers/2003/00000069/00000004/art00004)|Improved CVA, Double-Window Flexible Pace Search (DFPS), minimum-distance categorizing technique|Multitemporal Landsat TM Images of the Haidian District, Beijing, China|\n|1998|[MAD](http://people.compute.dtu.dk/alan/software.html)|Multivariate Alteration Detection (MAD) and MAF Postprocessing in Multispectral, Bitemporal Image Data: New Approaches to Change Detection Studies|[RSE1998](https://www.sciencedirect.com/science/article/pii/S0034425797001624)|Multivariate Alteration Detection (MAD), Maximum Autocorrelation Factor (MAF), Canonical Correlation Analysis (CCA)|Two Landsat MSS images of Queensland, Australia; Two AVHRR images of El Niño stages in California Current System|\n|1980|CVA|Change Vector Analysis: An Approach for Detecting Forest Changes with Landsat|[LARS symposia 1980](https://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1386\u0026context=lars_symp)|Change Vector Analysis, Forest Change Detection, Landsat Multispectral Data, Spatial-Spectral Clustering, Tasseled Cap Transformation| Landsat data covering three timber compartments in the Palouse District of Clearwater National Forest, Idaho|\n\n\n# Review Papers\n\n| Year | Title | Publication | Description |\n| :--- | :--- | :--- | :--- | \n|2025|遥感智能变化检测的深度学习方法：演变与发展趋势|[测绘学报2025](https://mp.weixin.qq.com/s/jZp_79g-L_LTCJpNRDOMdQ)|本文系统综述了深度学习在遥感变化检测中的研究进展，围绕变化特征表达和网络学习策略两大核心问题，梳理了从局部到时空联合、单一到多模态、轻量到大模型、二值到多类别特征表达的发展趋势，以及从全监督向弱/半监督和无监督学习的演进路径，并指出图文融合、生成式模型和人机协同是未来提升智能化水平的关键方向。|\n|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](http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2025.4.0783)|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。|\n| 2025 | On the use of Graphs for Satellite Image Time Series | [arXiv2025](https://arxiv.org/abs/2505.16685) | 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. | \n| 2025 | A Survey of Sample-Efficient Deep Learning for Change Detection in Remote Sensing: Tasks, Strategies, and Challenges | [GRSM2025](https://ieeexplore.ieee.org/abstract/document/10884556) | 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. | \n| 2025 | Deep learning change detection techniques for optical remote sensing imagery: Status, perspectives and challenges | [JAG2025](https://www.sciencedirect.com/science/article/pii/S1569843224006381) | 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](https://github.com/daifeng2016/Awesome-Optical-Remote-Sensing-Datasets-and-Methods). |\n| 2024 | Deep-Learning for Change Detection Using Multi-Modal Fusion of Remote Sensing Images: A Review | [RS2024](https://www.mdpi.com/2072-4292/16/20/3852) | 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. | \n| 2024 | Deep Learning for Satellite Image Time-Series Analysis: A review | [GRSM2024](https://ieeexplore.ieee.org/abstract/document/10529247) | 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. | \n| 2024 | Advances and Challenges in Deep Learning-Based Change Detection for Remote Sensing Images: A Review through Various Learning Paradigms | [RS2024](https://www.mdpi.com/2072-4292/16/5/804) | 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. | \n| 2024 | Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review | [RS2024](https://www.mdpi.com/2072-4292/16/13/2355) | 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. | \n| 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](https://www.ygxb.ac.cn/zh/article/doi/10.11834/jrs.20222156/) | 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。 | \n| 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](https://www.ygxb.ac.cn/zh/article/doi/10.11834/jrs.20222199/) | 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](https://www.bilibili.com/video/BV1Cf4y1Y77x/?vd_source=22b45bd19426f7fd4ee5b0e1055bfc8c)。 | \n| 2023 | 3D urban object change detection from aerial and terrestrial point clouds: A review | [JAG2023](https://www.sciencedirect.com/science/article/pii/S1569843223000808) | 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. | \n| 2023 | Change detection of urban objects using 3D point clouds: A review | [ISPRS P\u0026RS 2023](https://www.sciencedirect.com/science/article/pii/S0924271623000163) | 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. | \n|2022|Land Cover Change Detection With Heterogeneous Remote Sensing Images: Review, Progress, and Perspective|[IEEE PROC 2022](https://ieeexplore.ieee.org/document/9955391)|Provides a comprehensive overview of heterogeneous remote sensing image change detection (Hete-CD), summarizing its literature, major techniques, datasets, performance evaluations, challenges, and future directions to serve as a one-stop reference for researchers and practitioners.|\n|2022|Deep learning for change detection in remote sensing: a review|[GSIS2022](https://www.tandfonline.com/doi/full/10.1080/10095020.2022.2085633)|Analyzes why deep learning enhances remote sensing change detection by examining its improved information representation, methodological advances, and performance gains across spectral, spatial, temporal, and multi-sensor dimensions, while also identifying key limitations and future directions for deep learning change detection development.|\n| 2022 | Land Cover Change Detection Techniques: Very-high-resolution optical images: A review | [GRSM2022](https://ieeexplore.ieee.org/document/9477629) | 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. | \n| 2022 | A Survey on Deep Learning-Based Change Detection from High-Resolution Remote Sensing Images | [RS2022](https://www.mdpi.com/2072-4292/14/7/1552) | 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. | \n|2022|A review of multi-class change detection for satellite remote sensing imagery|[GSIS2022](https://www.tandfonline.com/doi/full/10.1080/10095020.2022.2128902)|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.|\n|2021|Change Detection From Very-High-Spatial-Resolution Optical Remote Sensing Images: Methods, applications, and future directions|[GRSM2021](https://ieeexplore.ieee.org/abstract/document/9395350)|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.|\n| 2020 | A survey of change detection methods based on remote sensing images for multi-source and multi-objective scenarios | [RS2020](https://www.mdpi.com/2072-4292/12/15/2460) | 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. | \n| 2020 | Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges | [RS2020](https://www.mdpi.com/2072-4292/12/10/1688) | 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](https://github.com/MinZHANG-WHU/Change-Detection-Review).| \n| 2019 | A Review of Change Detection in Multitemporal Hyperspectral Images: Current Techniques, Applications, and Challenges | [GRSM2019](https://ieeexplore.ieee.org/abstract/document/8738052) | 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. | \n|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](http://ch.whu.edu.cn/article/id/6272)|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。|\n|2017|多时相遥感影像变化检测的现状与展望|[测绘学报2017](https://html.rhhz.net/CHXB/html/2017-10-1447.htm)|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。|\n| 2017 | Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications | [ISPRS P\u0026RS 2017](https://www.sciencedirect.com/science/article/pii/S092427161730103X) | 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. | \n| 2016 | Optical remotely sensed time series data for land cover classification: A review | [ISPRS P\u0026RS 2016](https://www.sciencedirect.com/science/article/pii/S0924271616000769) | 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. | \n|2016|SAR影像变化检测研究进展|[计算机研究与发展2015](https://crad.ict.ac.cn/cn/article/Y2016/I1/123)|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。|\n| 2015 | A critical synthesis of remotely sensed optical image change detection techniques | [RSE2015](https://www.sciencedirect.com/science/article/pii/S0034425715000152) | 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. | \n| 2013 | Change detection from remotely sensed images: From pixel-based to object-based approaches | [ISPRS P\u0026RS 2013](https://www.sciencedirect.com/science/article/pii/S0924271613000804) | 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. | \n| 2012 | Object-based change detection | [IJRS2012](https://www.tandfonline.com/doi/abs/10.1080/01431161.2011.648285) | 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. | \n| 2012 | A review of large area monitoring of land cover change using Landsat data | [RSE2012](https://www.sciencedirect.com/science/article/pii/S0034425712000314) | 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. | \n|2011|多时相遥感影像变化检测综述|[地理信息世界2011](https://d.wanfangdata.com.cn/periodical/Ch9QZXJpb2RpY2FsQ0hJTmV3UzIwMjUwMTE2MTYzNjE0Eg9kbHh4c2oyMDExMDIwMDcaCDZ4ejNuZmt5)|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。|\n| 2005 | Image change detection algorithms: a systematic survey | [TIP2004](https://ieeexplore.ieee.org/document/1395984) | 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. | \n| 2004 | Digital change detection methods in ecosystem monitoring: a review | [IJRS2004](https://www.tandfonline.com/doi/abs/10.1080/0143116031000101675) | 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. | \n| 2004 | Change detection techniques | [IJRS2004](https://www.tandfonline.com/doi/abs/10.1080/0143116031000139863) | 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. | \n|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](http://ch.whu.edu.cn/cn/article/pdf/preview/4718.pdf)|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。|\n\n\n# Competitions\n\n| Year | Target | Contest | Track | Image Pairs | Image Size | Resolution |Other|\n| --- | --- | --- | --- | --- | --- | --- | --- |\n|2025|Building|[AI for Earthquake Response](https://platform.ai4eo.eu/ai-for-earthquake-response)|Detect damaged vs. undamaged buildings by analyzing high-resolution pre- and post-event satellite imagery|-|-|-|-|\n| 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](https://www.gaofen-challenge.com/challenge) |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 |-|\n| 2024 | Land cover | [“吉林一号”杯卫星遥感应用青年创新创业大赛](https://www.jl1mall.com/contest/matchMenu) |高分辨率遥感影像全要素变化检测研究| 5,000 | 512×512 |\u003c0.75m|-|\n| 2023 | Cropland | [“吉林一号”杯卫星遥感应用青年创新创业大赛](https://www.jl1mall.com/contest/match/info?id=1645664411716952066) |基于高分辨率卫星影像的耕地变化检测| 8,000 | 256×256 |\u003c0.75m|-|\n| 2023 | Land cover | [“国丰东方慧眼杯”遥感影像智能处理算法大赛](http://rsipac.whu.edu.cn/) |对象级变化检测| \u003e6,000 | 512×512 | 1-2m |-|\n| 2022 | Land cover | [“航天宏图杯”遥感影像智能处理算法大赛](http://rsipac.whu.edu.cn/) |遥感影像变化检测| \u003e6,000 | 512×512 | 1-2m |-|\n| 2022 | Flood | [SpaceNet8: Flood Detection Challenge](https://join.topcoder.com/spacenet) | Flood Detection Challenge Using Multiclass Segmentation | 12 | 1,300×1,300 | 0.3-0.8m |[Dataset Paper](https://openaccess.thecvf.com/content/CVPR2022W/EarthVision/papers/Hansch_SpaceNet_8_-_The_Detection_of_Flooded_Roads_and_Buildings_CVPRW_2022_paper.pdf), [Solution Paper](https://ieeexplore.ieee.org/document/10281500)|\n| 2021 |Land cover|[IEEE GRSS Data Fusion Contest](https://www.grss-ieee.org/community/technical-committees/2021-ieee-grss-data-fusion-contest-track-msd/)|Multitemporal Semantic Change Detection|2,250|-|-|[Outcome Paper](https://ieeexplore.ieee.org/document/9690575)|\n| 2021 |Land cover|[DynamicEarthNet Challenge](https://codalab.lisn.upsaclay.fr/competitions/2882) | Weakly-Supervised Unsupervised Binary Land Cover Change Detection, Multi-Class Change Detection|54,750|1,024x1,024|3.0|[Top1 Solution](https://github.com/solcummings/earthvision2021-weakly-supervised), [Dataset Paper](https://openaccess.thecvf.com/content/CVPR2022/papers/Toker_DynamicEarthNet_Daily_Multi-Spectral_Satellite_Dataset_for_Semantic_Change_Segmentation_CVPR_2022_paper.pdf)|\n| 2021 | Land cover | [“昇腾杯”遥感影像智能处理算法大赛](http://rsipac.whu.edu.cn/subject_two_2021) | 耕地建筑物变化检测 | \u003e6,000 | 512×512 | 1-2m |[Top4 Solution](https://github.com/WangZhenqing-RS/2021rsipac_changeDetection_TOP4), [Top5 Solution](https://github.com/78666621/2021rsipac_changeDetection_TOP5)|\n| 2021 | Building | [遥感图像智能解译技术挑战赛](https://captain-whu.github.io/PRCV2021_RS/tasks.html) | 遥感图像建筑物变化检测 | 10,000 | 512×512 | - |-|[Top2 Solution](https://github.com/businiaoo/PRCV2021-Change-Detection-Contest-2nd-place-Solution), [Top3 Solution](https://github.com/likyoo/PRCV2021_ChangeDetection_Top3)|\n| 2021 | Building | [慧眼“天智杯”人工智能挑战赛](https://rsaicp.com/portal/contestList) |可见光建筑智能变化检测| 5,000 | 1,024×1,024 | 0.5-0.7m |-|\n| 2020 | Land cover | [商汤科技首届AI遥感解译大赛](https://senseearth-cloud.com/) |变化检测|4,662 | 512×512 | 0.5-3m |[Top1 Solution](https://github.com/LiheYoung/SenseEarth2020-ChangeDetection)|\n| 2020 | Land cover | [SpaceNet 7: Multi-Temporal Urban Development Challenge](https://medium.com/the-downlinq/the-spacenet-7-multi-temporal-urban-development-challenge-dataset-release-9e6e5f65c8d5) | Multi-Temporal Urban Development Challenge |-|1,024×1,024| 4m |[Solutions](https://github.com/SpaceNetChallenge/SpaceNet7_Multi-Temporal_Solutions), [Dataset Paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Van_Etten_The_Multi-Temporal_Urban_Development_SpaceNet_Dataset_CVPR_2021_paper.pdf)|\n| 2019 | Building | [xView2 Challenge](https://xview2.org/dataset) (or xBD) | Building Damage Assessment | 11,034 | 1,024×1,024 | - |[Dataset Paper](https://arxiv.org/abs/1911.09296)|\n\n\n# Satellite Data Resources for Disaster Response\n\n| Name | Description |\n| --- | --- | \n|[Maxar Open Data Program](https://www.maxar.com/open-data)  |The Maxar Open Data Program provides pre- and post-event satellite imagery (from WorldView-3 and other sensors) for select sudden-onset major crises, along with crowdsourced damage assessments.|\n|[吉林一号资源库](https://www.jl1mall.com/resrepo/?fromUrl=https://www.jl1mall.com/edu)|提供高分辨率卫星影像和专题数据，支持自然灾害监测、农业估产、生态环境保护、水利管理及应急响应等多领域应用。部分数据集仅限教育认证用户。|\n|[Planet Disaster Datasets](https://www.planet.com/disasterdata/) |Planet makes available select imagery for major disaster events, including major earthquakes, floods, storms, wildfires, and human-made disasters. To download the data, users must complete a form for access qualification.|\n|[The International Charter: Space And Major Disasters](https://disasterscharter.org/)|Disaster mapping results and analyses are available for various global hazards, but the underlying satellite imagery is not directly provided.|\n\n\n# More Resources\n\n| Name | Description |\n| --- | --- | \n|[Hansen Global Forest Change](https://glad.earthengine.app/view/global-forest-change) ([GEE dataset](https://developers.google.com/earth-engine/datasets/catalog/UMD_hansen_global_forest_change_2023_v1_11))|Annual global tree cover loss and gain maps at 30m resolution (2000–present), widely used as ground-truth labels and evaluation data for forest change detection research. Produced by the GLAD lab at the University of Maryland. Full-resolution GeoTIFFs are also available via [earthenginepartners.appspot.com](https://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.11.html).|\n|[daifeng2016/Awesome-Optical-Remote-Sensing-Datasets-and-Methods](https://github.com/daifeng2016/Awesome-Optical-Remote-Sensing-Datasets-and-Methods?tab=readme-ov-file)|This repository is for summarizing the latest optical remote sensing datasets and methods, which are listed in review article: *Deep learning change detection techniques for optical remote sensing imagery: Status, perspectives and challenges*, published in [JAG2025](https://www.sciencedirect.com/science/article/pii/S1569843224006381).|\n|[MinZHANG-WHU/Change-Detection-Review](https://github.com/MinZHANG-WHU/Change-Detection-Review)|A review of change detection methods, including codes and open datasets for deep learning. From paper: *Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges*, published in [RS2020](https://www.mdpi.com/2072-4292/12/10/1688). |\n|[DoongLi/Awesome-Scene-Change-Detection](https://github.com/DoongLi/Awesome-Scene-Change-Detection)|This repository curates a comprehensive list of resources for scene change detection, including papers, videos, code, and relevant websites. While many change detection studies focus on remote sensing, this collection is specifically dedicated to works tested on street-view scenes and primarily covers methods based on robot vision (especially using image and point cloud data)|\n\n\n# Citation\n\nIf you find our project useful in your research, please consider citing:\n\n```latex\n@misc{awesome_rscd_2019,\n    title={Awesome Remote Sensing Change Detection},\n    author={Awesome RSCD Contributors},\n    howpublished = {\\url{https://github.com/wenhwu/awesome-remote-sensing-change-detection}},\n    year={2019}\n}\n```\n","funding_links":[],"readme_doi_urls":["https://doi.org/10.1016/j.jag.2026.105125"],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":[],"project_url":"https://ost.ecosyste.ms/api/v1/projects/20930","html_url":"https://ost.ecosyste.ms/projects/20930"}