3D

Without Label

• 3DCD Dataset
• Marsocci V et al.2023
• URB3DCD
• the first and second districts of Lyon - 4292/13/13/2629)
• the first and second districts of Lyon - 4292/13/13/2629)

Deep Learning

CNNs

• RIEM - source data||
• Semantic-TemporalNet - TemporalNet: A Novel Urban Block Change Detection Method Based on Semantic Coherence Analysis|TGRS2025|Sentinel-2, time-series semantic coherence, urban renewal|Changsha and Wuhan Sentinel-2 imagery||
• ISPRS P&RS2025 - 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) |-|
• RSE2024 - source images, Cross-task transform|Gaofen-2, Gaofen-3, Sentinel-1, and Sentinel-2|-|
• STMNet - Temporal Mask-Based Network for Self-Supervised Hyperspectral Change Detection|TGRS2024|hyperspectral image, multiscale feature, single temporal, mask|Farmland dataset, Hermiston dataset, Bay dataset||
• ClearSCD - task learning, contrastive learning, change vector analysis|Hi-UCD, LsSCD| |
• SSLChange - Supervised Change Detection Framework Based on Domain Adaptation| TGRS2024| Domain adaption, hierarchical features, image contrastive learning|CDD, LEVIR-CD| |
• ISPRS P&RS 2024 - CD, xBD, SECOND, SYSU-CD, Multi-temporal xBD|-|
• RACDNet - arbitrary remote sensing change detection with Spatial-frequency dual domain learning|ISPRS P&RS 2025|Resolution-arbitrary change detection; Gradient prior; Dual-domain learning|WHU-CD, LEVIR-CD, SYSU-CD, and Google dataset||
• PRO-HRSCD - SCD||
• Bi-SRNet - temporal semantic reasoning for the semantic change detection in HR remote sensing images | TGRS2022 | Triple-branch, semantic correlations| SECOND| |
• SeCo - Training from Uncurated Remote Sensing Data | ICCV2021 | Self-supervised learning| BigEarthNet, EuroSAT, OSCD | |
• SNUNet-CD - CD: A Densely Connected Siamese Network for Change Detection of VHR Images | GRSL2021 | Fully convolutional siamese network | CDD | |
• ISPRS P&RS2025 - 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) |-|
• RSE2024 - source images, Cross-task transform|Gaofen-2, Gaofen-3, Sentinel-1, and Sentinel-2|-|
• ISPRS P&RS 2024 - CD, xBD, SECOND, SYSU-CD, Multi-temporal xBD|-|
• Shield - Affected Areas by Combining the Strengths of Change Detection and Anomaly Detection: Target for On-Orbit Application|JRS2026|On-orbit computing, Unsupervised change detection, anomaly detection| Landslide-Brazil, wildfire-Russia, flood-China-Hebi, and deforestation-New Zealand||

Diffusion Models and GANs

• CT2Net - Based Collaborative Training for Hyperspectral-RGB Multimodal Change Detection|TPAMI2025|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)||
• RS-NormGAN - NormGAN: Enhancing change detection of multi-temporal optical remote sensing images through effective radiometric normalization|ISPRS P&RS 2025|Deep style transfer; Domain adaptation; GAN; Multi-temporal radiometric normalization|GESD, SHCD||
• UP-Diff - Diff: Latent Diffusion Model for Remote Sensing Urban Prediction|GRSL2024|Cross-attention, latent diffusion model, urban planning|LEVIR-CD, SYSU-CD||
• ChangeDiff - Temporal Change Detection Data Generator with Flexible Text Prompts via Diffusion Model|AAAI2025|Diffusion models, text-to-layout model, multi-class distribution-guided text prompts|SECOND, Landsat-SCD, and HRSCD||
• Changen2 - Temporal Remote Sensing Generative Change Foundation Model | TPAMI2024 | Synthetic data pre-training, generative model, foundation model | WHU-CD, xBD, LEVIR-CD, S2Looking, SECOND| |
• BDGF - Guided Fusion for Multimodal Remote Sensing Classification|arXiv2025|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)||

Foundation Models

• GeoVLM-R1 - R1: Reinforcement Fine-Tuning for Improved Remote Sensing Reasoning|arXiv2025|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||
• arXiv2025 - VL-7B|Remote sensing change understanding, Interactive multi-task instruction dataset, Vision-language models|ChangeIMTI (constructed from LEVIR-CC, LEVIR-MCI)|-|
• SegChange-R1 - R1: LLM-Augmented Remote Sensing Change Detection|arXiv2025|Swin Transformer, Microsoft/Phi-1.5|LLM-augmented inference approach, Linear attention-based spatial transformation module|WHU-CD, CDD, DSIFN-CD, DVCD||
• SAM2-CD - CD: Remote Sensing Image Change Detection With SAM2|JSTAR2025|SAM2|Dynamic feature selection, global–local attention|WHU-CD, LEVIR-CD, and LEVIR-CD+||
• ViTP - Specific Foundation Models|arXiv2025|ViT, InternVL-2.5|Leveraging reasoning to enhance perception, ViT, Visual Robustness Learning|16 challenging remote sensing and medical imaging benchmarks ||
• AdaptVFMs-RSCD - RSCD: Advancing Remote Sensing Change Detection from binary to semantic with SAM and CLIP|ISPRS P&RS 2025|CLIP, SAM|Remote sensing VFM fine-tuning dataset|RS VFM Fine-tuning dataset, DSIFN-CD, CLCD, SYSU-CD||
• PeftCD - Efficient Fine-Tuning for Remote Sensing Change Detection|arXiv2025|SAM2, DINOv3|Vision Foundation Models, Parameter-Efficient Fine-Tuning|WHU-CD, CDD, LEVIR-CD, SYSU-CD, MSRSCD, MLCD, S2Looking||
• ISPRS P&RS 2025 - CD, HRCUS-CD, WRCD|-|
• SA-CDNet - 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 ||
• DynamicEarth - Vocabulary Change Detection? | arXiv2025|SAM2, DINOv2| Open-Vocabulary Change Detection| WHU-CD, LEVIR-CD, SECOND, S2Looking, and BANDON||
• EFI-SAM - Based Efficient Feature Integration Network for Remote Sensing Change Detection: A Case Study on Macao Sea Reclamation|JSTARS2025|SAM|Random Fourier features, sea reclamation|CLCD, SYSU-CD, S2Looking, MLCD||
• AnyChange - Paper-Conference.pdf) | SAM| Zero-shot change detection, bitemporal latent matching|xBD, LEVIR-CD, S2Looking, SECOND | |
• SCM - CD, LEVIR-CD| |
• JAG2025 - Anything; Multi-task learning|WHU-CD, LEVIR-CD, SECOND|-|
• SAM-CD - CD, LEVIR-CD, CLCD, S2Looking| |
• ISPRS P&RS 2025 - CD, HRCUS-CD, WRCD|-|

Transformers

• GSTM-SCD - SCD: Graph-enhanced spatio-temporal state space model for semantic change detection in multi-temporal remote sensing images|ISPRS P&RS 2025|State space model, Graph optimization, Spatio-temporal modeling|SECOND, Landsat-SCD, WUSU and DynamicEarthNet||
• Change3D - CD, HRSCD, xBD, LEVIR-CD, CLCD, SECOND, LEVIR-CC, and DUBAI-CC||
• UA-BCD - related theory, Building change detection mapping, Siamese pyramid vision transformer|CDD, WHU-CD, GZ-CD, LEVIR-CD, SYSU-CD| |
• RSE2024 - temporal learning, semantic change detection | xBD, Turkey–Syria earthquake (2023), Kalehe DRC flooding (2023), Maui Hawaii fire (2023) | - |
• ChangeSparse - commit/Z-Zheng/pytorch-change-models.svg?style=flat&logo=github&label=Last%20Commit) |
• ChangeStar2 - Temporal Supervised Learning for Universal Remote Sensing Change Detection | IJCV2024 | Universal change detection, single-temporal supervised learning |WHU-CD, CDD, xBD, LEVIR-CD, S2Looking, SpaceNet8, DynamicEarthNet, SECOND| |
• BiFA - CD, LEVIR-CD, LEVIR-CD+, SYSU-CD, DSIFN-CD, and CLCD| |
• CDMamba - CD, CDD, LEVIR-CD, LEVIR-CD+, and CLCD| |
• MaskCD - attention|LEVIR-CD, CLCD, SYSU-CD, EGY-BCD, and GVLM-CD| |
• M-CD - based Siamese Network for Remote Sensing Change Detection | arXiv2024 | Mamba, state space model, difference Module| WHU-CD, CDD, DSIFN-CD, and LEVIR-CD| |
• ScratchFormer - attention operation, change-enhanced feature fusion, | WHU-CD, OSCD, CDD, DSIFN-CD, and LEVIR-CD| |
• SitsSCD - commit/ElliotVincent/SitsSCD.svg?style=flat&logo=github&label=Last%20Commit) |
• ChangeMask - task Encoder-Transformer-Decoder Architecture for Semantic Change Detection | ISPRS P&RS 2022 | Multi-task learning, temporal symmetry, multi-temporal|SECOND, Hi-UCD| |
• BTC - CD, LEVIR-CD, EGY-BCD, GVLM-CD, CLCD, OSCD||
• CMNet - CD, LEVIR-CD, SYSU-CD||
• MBUKG_DCKRL_CD - source data representation learning, dual cross-attention mechanism|A comprehensive dataset comprising VHR images, POI data, and SVI|-|
• S-cCDNet - centric change detection framework: considering spatiotemporal heterogeneity and spatiotemporal correlation of land cover|IJDE2025|Multi-task learning, prototype representation, spatiotemporal heterogeneity, spatiotemporal correlation|SECOND, Landsat-SCD||
• SMGNet - Guided Multitask Neural Network for Remote Sensing Image Semantic Change Detection|GRSL2025|Historical semantic information, pseudochanges, semantic map-guided network, underdetection|HRSCD||
• HGINet - resolution remote sensing images|ISPRS P&RS 2024|Hierarchical semantic graph interaction network; Temporal correlations; Semantic difference interaction|SECOND, HRSCD, Fuzhou, and Xiamen||
• RSE2024 - temporal learning, semantic change detection | xBD, Turkey–Syria earthquake (2023), Kalehe DRC flooding (2023), Maui Hawaii fire (2023) | - |

Hyperspectral

Deep Learning

• **2023 “吉林一号”杯卫星遥感应用青年创新创业大赛-基于高分辨率卫星影像的耕地变化检测**
• **2022 The SpaceNet 8 Flood Detection Challenge**
• **2021 遥感图像智能解译技术挑战赛-建筑物变化检测**
• **2021 第五届“中科星图杯”国际高分遥感图像解译大赛-高分辨率可见光图像中建筑物普查与变化检测**
• **2021 慧眼“天智杯”人工智能挑战赛-可见光建筑智能变化检测**
• Li Z et al. 2022
• **2020 商汤科技首届AI遥感解译大赛-变化检测赛道**
• Gupta et al. 2019
• **2019 遥感图像稀疏表征与智能分析竞赛-变化检测赛道**
• **2017 广东政务数据创新大赛—智能算法赛**
• 天池
• Wang Y, Hong D, Sha J, et al. Spectral–spatial–temporal transformers for hyperspectral image change detection
• Hu M, Wu C, Zhang L, et al. Hyperspectral anomaly change detection based on autoencoder
• SpaceNetChallenge/SpaceNet8 - _The_Detection_of_Flooded_Roads_and_Buildings_CVPRW_2022_paper.pdf).
• WangZhenqing-RS/2021rsipac_changeDetection_TOP4
• solcummings/earthvision2021-weakly-supervised
• SpaceNetChallenge/SpaceNet7_Multi-Temporal_Solutions
• LiheYoung/SenseEarth2020-ChangeDetection
• chrieke/awesome-satellite-imagery-datasets
• MinZHANG-WHU/Change-Detection-Review

Without Label

• Hyperspectral Change Detection Dataset
• López-Fandiño et al.2018
• GETNET
• Wang et al.2018

Multi-Modal and SAR Datasets

• DisasterM3 - SAR-Instruction |-|-|-|Global|-|
• BRIGHT - 1m|Global|4|
• Hi-BCD
• UrbanSARFloods - 1_SLC-Based_Benchmark_Dataset_for_Urban_and_Open-Area_Flood_CVPRW_2024_paper.html) | Sentinel-1|8,879| 512×512|20m|Global|5|
• EVLab-CMCD - 2, BJ-2, Historical land use maps|5,622|512×512| 0.8m| 10 Cities in China|13|
• CAU-Flood - 1, Sentinel-2|18,302| 256×256|10m|Global|2|
• Kuro Siwo - Abstract-Datasets_and_Benchmarks_Track.html)|Sentinel-1, DEM| 67,490 |224×224| 10m|Global|3|
• Wuhan - 2, COSMO-SkyMed|1| 11,216×13,693|3m |Wuhan, China|2|
• Ombria - 1, Sentinel-2| 1,688| 256×256| 10m|Global|2|
• rschange - source toolbox dedicated to reproducing and developing advanced methods (e.g., DDLNet, CDMask) for change detection in remote sensing images.||

Multispectral

Deep Learning

• Fang S, Li K, Li Z. Changer: Feature Interaction is What You Need for Change Detection
• Saha S, Ebel P, Zhu X X. Self-supervised multisensor change detection
• Open-CD
• torchange
• Li K, Cao X, Deng Y, et al. DiffMatch: Visual-Language Guidance Makes Better Semi-supervised Change Detector
• Chen H, Song J, Han C, et al. Changemamba: Remote sensing change detection with spatio-temporal state space model
• Dong S, Wang L, Du B, et al. ChangeCLIP: Remote sensing change detection with multimodal vision-language representation learning
• Corley I, Robinson C, Ortiz A. A Change Detection Reality Check
• Ye Y, Wang M, Zhou L, et al. Adjacent-level feature cross-fusion with 3D CNN for remote sensing image change detection
• Li K, Cao X, Meng D. A New Learning Paradigm for Foundation Model-based Remote Sensing Change Detection
• Ding L, Zhu K, Peng D, et al. Adapting Segment Anything Model for Change Detection in HR Remote Sensing Images
• Zheng Z, Tian S, Ma A, et al. Scalable Multi-Temporal Remote Sensing Change Data Generation via Simulating Stochastic Change Process
• Bernhard M, Strauß N, Schubert M. MapFormer: Boosting Change Detection by Using Pre-change Information
• Mall U, Hariharan B, Bala K. Change-Aware Sampling and Contrastive Learning for Satellite Images
• Chen H, Song J, Wu C, et al. Exchange means change: An unsupervised single-temporal change detection framework based on intra-and inter-image patch exchange
• Xing Y, Jiang J, Xiang J, et al. LightCDNet: Lightweight Change Detection Network Based on VHR Images
• Lei T, Geng X, Ning H, et al. Ultralightweight Spatial–Spectral Feature Cooperation Network for Change Detection in Remote Sensing Images
• Li Z, Tang C, Liu X, et al. Lightweight Remote Sensing Change Detection with Progressive Feature Aggregation and Supervised Attention
• Feng Y, Jiang J, Xu H, et al. Change Detection on Remote Sensing Images using Dual-branch Multi-level Inter-temporal Network
• Seo M, Lee H, Jeon Y, et al. Self-Pair: Synthesizing Changes from Single Source for Object Change Detection in Remote Sensing Imagery
• Chen H, Pu F, Yang R, et al. RDP-Net: Region detail preserving network for change detection
• Cao Y, Huang X. A full-level fused cross-task transfer learning method for building change detection using noise-robust pretrained networks on crowdsourced labels
• Chen H, Li W, Chen S, et al. Semantic-aware Dense Representation Learning for Remote Sensing Image Change Detection
• Codegoni A, Lombardi G, Ferrari A. TINYCD: A (Not So) Deep Learning Model For Change Detection
• Pei G, Zhang L. Feature Hierarchical Differentiation for Remote Sensing Image Change Detection
• shinianzhihou/ChangeDetection
• Liu J, Xuan W, Gan Y, et al. An End-to-end Supervised Domain Adaptation Framework for Cross-Domain Change Detection
• PaddleRS
• Ding L, Guo H, Liu S, et al. Bi-temporal semantic reasoning for the semantic change detection in HR remote sensing images
• Bandara W G C, Nair N G, Patel V M. DDPM-CD: Remote Sensing Change Detection using Denoising Diffusion Probabilistic Models
• Bandara W G C, Patel V M. Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Images
• Bandara W G C, Patel V M. A Transformer-Based Siamese Network for Change Detection
• Chen P, Hong D, Chen Z, et al. FCCDN: Feature Constraint Network for VHR Image Change Detection
• Bobholamovic/CDLab - based change detection methods
• Shi Q, Liu M, Li S, et al. A Deeply Supervised Attention Metric-Based Network and an Open Aerial Image Dataset for Remote Sensing Change Detection
• Diakogiannis F I, Waldner F, Caccetta P. Looking for change? Roll the Dice and demand Attention
• Papadomanolaki M, Vakalopoulou M, Karantzalos K. A Deep Multi-Task Learning Framework Coupling Semantic Segmentation and Fully Convolutional LSTM Networks for Urban Change Detection
• Zheng Z, Zhong Y, Wang J, et al. Building damage assessment for rapid disaster response with a deep object-based semantic change detection framework: from natural disasters to man-made disasters
• Xu J, Luo C, Chen X, et al. Remote Sensing Change Detection Based on Multidirectional Adaptive Feature Fusion and Perceptual Similarity
• Zhang H, Lin M, Yang G, et al. ESCNet: An End-to-End Superpixel-Enhanced Change Detection Network for Very-High-Resolution Remote Sensing Images
• Zheng Z, Ma A, Zhang L, et al. Change is Everywhere: Single-Temporal Supervised Object Change Detection for High Spatial Resolution Remote Sensing Imagery
• Liu M, Shi Q, Marinoni A, et al. Super-resolution-based Change Detection Network with Stacked Attention Module for Images with Different Resolutions
• Chen H, Qi Z, Shi Z. Remote Sensing Image Change Detection with Transformers
• Chen H, Li W, Shi Z. Adversarial Instance Augmentation for Building Change Detection in Remote Sensing Images
• Fang S, Li K, Shao J, et al. SNUNet-CD: A Densely Connected Siamese Network for Change Detection of VHR Images
• ChenHongruixuan/ChangeDetectionRepository
• llu025/Heterogeneous_CD
• Daudt R C, Le Saux B, Boulch A. Fully convolutional siamese networks for change detection
• Marsocci V, Coletta V, Ravanelli R, et al. Inferring 3D change detection from bitemporal optical images
• de Gélis I, Lefèvre S, Corpetti T. Siamese KPConv: 3D multiple change detection from raw point clouds using deep learning

Traditional Method

• Li X, Liu X, Liang X, et al. Geographical Simulation and Optimization System (GeoSOS)
• 2009.Celik T. Unsupervised change detection in satellite images using principal component analysis and k-means clustering
• Nielsen A A.The Regularized Iteratively Reweighted Multivariate Alteration Detection (IR-MAD)
• Kondmann L, Toker A, Saha S, et al. SiROC: Spatial Context Awareness for Unsupervised Change Detection in Optical Satellite Images
• Lv Z, Wang F J, Liu T, et al. Novel Automatic Approach for Land Cover Change Detection by Using VHR Remote Sensing Images
• Bobholamovic/ChangeDetectionToolbox
• Canty M J. Image Analysis, Classification and Change Detection in Remote Sensing (Fourth Revised Edition)
• Canty M J. Change Detection with Google Earth Engine Imagery
• Canty M J. Image Analysis, Classification and Change Detection in Remote Sensing (Third Revised Edition)
• Zhu Z, Woodcock C E, et al. Algorithm developed for Continuous Change Detection and Classification (CCDC) of land cover using all available Landsat data
• Matlab - Detection-in-Satellite-Imagery)
• 2009.Celik T. Unsupervised change detection in satellite images using principal component analysis and k-means clustering

With Label

• **SMARS (Simulated Multimodal Aerial Remote Sensing) dataset**
• Reyes et al., 2023
• Zhang et al., 2023
• Zhang et al., 2023
• Holail et al.2023
• Liao et al.2023
• Zhou et al.2023
• Pang et al.2023
• Liu et al.2022
• Toker A et al.2022
• Ji et al., 2023
• Liu et al.2022
• Shen et al.2021
• Shi et al.2021
• Leenstra et al.2021
• Tian et al.2020
• Zhang et al.2020
• Yang et al.2020
• BIGEMAP - varying VHR images pairs with three bands of red, green, and blue, a spatial resolution of 0.55 m, and the size ranging from 1006×1168 pixels to 4936×5224 pixels. The annotation is focused on buildings. Paper: Peng et al.2020
• **LEVIR building Change Detection (LEVIR-CD) Dataset**
• Daudt et al.2019
• Ji et al.2018
• Daudt et al.2018
• Bourdis et al.2011
• **SZTAKI AirChange Benchmark set**
• Benedek et al.2008
• **SMARS (Simulated Multimodal Aerial Remote Sensing) dataset**
• Sun et al., 2024
• **ChangeNet**
• **SI-BU dataset**
• **Multisource built-up change (MSBC) and multisource OSCD (MSOSCD) datasets**
• **Multi-modal Supervised Change Detection Data**
• **Hi-UCD**

Without Label

• Planet Disaster Datasets
• LINZ DATA SERVICE

Optical Datasets

• DynamicEarthNet - Spectral_Satellite_Dataset_for_Semantic_Change_Segmentation_CVPR_2022_paper.html) |PlanetFusion|54,750|1,024×1,024|3m|Global|7|
• OSCD - 2|24|600×600|10-60m|Global|2|
• S2MTCP - 3-030-68787-8_42) |Sentinel-2|1,520|600×600|10m|Global|-|
• DSIFN - CD)|ISPRS P&RS 2020 |Google Earth|3,940 | 512×512|-|China (Beijing, Chengdu, Shenzhen, Chongqing, Wuhan, and Xian)|2|
• HRSCD