{"url":"/dataset/levir-cd","name":"LEVIR-CD","full_name":null,"description_markdown":"LEVIR-CD is a new large-scale remote sensing building Change Detection dataset. The introduced dataset would be a new benchmark for evaluating change detection (CD) algorithms, especially those based on deep learning.\r\n\r\nLEVIR-CD consists of 637 very high-resolution (VHR, 0.5m/pixel) Google Earth (GE) image patch pairs with a size of 1024 × 1024 pixels. These bitemporal images with time span of 5 to 14 years have significant land-use changes, especially the construction growth. LEVIR-CD covers various types of buildings, such as villa residences, tall apartments, small garages and large warehouses. Here, we focus on building-related changes, including the building growth (the change from soil/grass/hardened ground or building under construction to new build-up regions) and the building decline. These bitemporal images are annotated by remote sensing image interpretation experts using binary labels (1 for change and 0 for unchanged). Each sample in our dataset is annotated by one annotator and then double-checked by another to produce high-quality annotations. The fully annotated LEVIR-CD contains a total of 31,333 individual change-building instances.","description_withheld":null,"homepage":"https://justchenhao.github.io/LEVIR/","introduced_date":"2020-05-22","introduced_date_note":null,"introduced_by":{"paper":"/paper/a-spatial-temporal-attention-based-method-and","title":"A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection","first_author":"Hao Chen","url":null},"license":null,"modalities":[],"tasks":[{"name":"Change Detection","url":"/task/change-detection","datasets_with_task":"/datasets/task/change-detection"},{"name":"Building change detection for remote sensing images","url":"/task/building-change-detection-for-remote-sensing","datasets_with_task":"/datasets/task/building-change-detection-for-remote-sensing"}],"languages":[],"variants":["LEVIR-CD"],"data_loaders":[{"repo":"https://github.com/likyoo/open-cd","url":"https://github.com/likyoo/open-cd","frameworks":["pytorch"]},{"repo":"https://github.com/likyoo/change_detection.pytorch","url":"https://github.com/likyoo/change_detection.pytorch","frameworks":["pytorch"]}],"num_papers_in_archive":131,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/building-change-detection-for-remote-sensing","task":"Building change detection for remote sensing images","dataset_variant":"LEVIR-CD","rows":37,"metrics":["F1","IoU","Params(M)"],"first_row_in_archive_order":{"model":"MAE+MTP(ViT-L+RVSA)","paper":"/paper/mtp-advancing-remote-sensing-foundation-model","metrics":{"F1":"92.67","Params(M)":"305"},"code_links":[{"title":"vitae-transformer/mtp","url":"https://github.com/vitae-transformer/mtp"},{"title":"cuzyoung/crossearth","url":"https://github.com/cuzyoung/crossearth"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/change-detection-on-levir-cd","task":"Change Detection","dataset_variant":"LEVIR-CD","rows":28,"metrics":["F1","IoU","Overall Accuracy","F1-score","Recall","Precision"],"first_row_in_archive_order":{"model":"SChanger-base","paper":"/paper/schanger-change-detection-from-a-semantic","metrics":{"F1":"92.87"},"code_links":[{"title":"zhouziyu-cn/SChanger","url":"https://github.com/zhouziyu-cn/SChanger"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/be-the-change-you-want-to-see-revisiting","title":"Be the Change You Want to See: Revisiting Remote Sensing Change Detection Practices","date":"2025-07-04","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/schanger-change-detection-from-a-semantic","title":"SChanger: Change Detection from a Semantic Change and Spatial Consistency Perspective","date":"2025-03-26","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/lwganet-a-lightweight-group-attention","title":"LWGANet: A Lightweight Group Attention Backbone for Remote Sensing Visual Tasks","date":"2025-01-17","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/frequency-temporal-attention-network-for-1","title":"Frequency-Temporal Attention Network for Remote Sensing Imagery Change Detection","date":"2024-10-10","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/rethinking-remote-sensing-change-detection","title":"Rethinking Remote Sensing Change Detection With A Mask View","date":"2024-06-21","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/src-net-bi-temporal-spatial-relationship","title":"SRC-Net: Bi-Temporal Spatial Relationship Concerned Network for Change Detection","date":"2024-06-09","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/c2f-semicd-a-coarse-to-fine-semi-supervised","title":"C2F-SemiCD: A Coarse-to-Fine Semi-Supervised Change Detection Method Based on Consistency Regularization in High-Resolution Remote Sensing Images","date":"2024-04-22","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/hanet-a-hierarchical-attention-network-for","title":"HANet: A Hierarchical Attention Network for Change Detection With Bitemporal Very-High-Resolution Remote Sensing Images","date":"2024-04-14","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/change-guiding-network-incorporating-change","title":"Change Guiding Network: Incorporating Change Prior to Guide Change Detection in Remote Sensing Imagery","date":"2024-04-14","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/rs-mamba-for-large-remote-sensing-image-dense","title":"RS-Mamba for Large Remote Sensing Image Dense Prediction","date":"2024-04-03","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":2,"samples_unverified":4,"pointer_only_for_licence":6,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/mtp-advancing-remote-sensing-foundation-model","title":"MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining","date":"2024-03-20","rows_on_this_dataset":6,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":4,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/lsknet-a-foundation-lightweight-backbone-for","title":"LSKNet: A Foundation Lightweight Backbone for Remote Sensing","date":"2024-03-18","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/bifa-remote-sensing-image-change-detection","title":"BiFA: Remote Sensing Image Change Detection With Bitemporal Feature Alignment","date":"2024-03-18","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/maskchanger-a-transformer-based-model","title":"MaskChanger: A Transformer-Based Model Tailoring Change Detection with Mask Classification","date":"2024-03-06","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/changeclip-remote-sensing-change-detection","title":"ChangeCLIP: Remote sensing change detection with multimodal vision-language representation learning","date":"2024-01-04","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/time-travelling-pixels-bitemporal-features","title":"Time Travelling Pixels: Bitemporal Features Integration with Foundation Model for Remote Sensing Image Change Detection","date":"2023-12-23","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/a-new-learning-paradigm-for-foundation-model","title":"A New Learning Paradigm for Foundation Model-based Remote Sensing Change Detection","date":"2023-12-02","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/exchanging-dual-encoder-decoder-a-new","title":"Exchanging Dual Encoder-Decoder: A New Strategy for Change Detection with Semantic Guidance and Spatial Localization","date":"2023-11-19","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/lightcdnet-lightweight-change-detection","title":"LightCDNet: Lightweight Change Detection Network Based on VHR Images","date":"2023-08-11","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/ultralightweight-spatial-spectral-feature","title":"Ultralightweight Spatial–Spectral Feature Cooperation Network for Change Detection in Remote Sensing Images","date":"2023-03-24","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/hcgmnet-a-hierarchical-change-guiding-map","title":"HCGMNET: A Hierarchical Change Guiding Map Network For Change Detection","date":"2023-02-21","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/transition-is-a-process-pair-to-video-change","title":"Transition Is a Process: Pair-to-Video Change Detection Networks for Very High Resolution Remote Sensing Images","date":"2022-12-07","rows_on_this_dataset":2,"code_links":2,"syntology":null},{"paper":"/paper/saras-net-scale-and-relation-aware-siamese","title":"SARAS-Net: Scale and Relation Aware Siamese Network for Change Detection","date":"2022-12-02","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/changer-feature-interaction-is-what-you-need","title":"Changer: Feature Interaction is What You Need for Change Detection","date":"2022-09-17","rows_on_this_dataset":6,"code_links":1,"syntology":null},{"paper":"/paper/siamixformer-a-siamese-transformer-network","title":"SiamixFormer: a fully-transformer Siamese network with temporal Fusion for accurate building detection and change detection in bi-temporal remote sensing images","date":"2022-08-01","rows_on_this_dataset":6,"code_links":0,"syntology":null},{"paper":"/paper/tinycd-a-not-so-deep-learning-model-for","title":"TINYCD: A (Not So) Deep Learning Model For Change Detection","date":"2022-07-26","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/remote-sensing-change-detection-segmentation","title":"DDPM-CD: Denoising Diffusion Probabilistic Models as Feature Extractors for Change Detection","date":"2022-06-23","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":13,"samples_ran":10,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/an-empirical-study-of-remote-sensing","title":"An Empirical Study of Remote Sensing Pretraining","date":"2022-04-06","rows_on_this_dataset":4,"code_links":2,"syntology":null},{"paper":"/paper/a-transformer-based-siamese-network-for","title":"A Transformer-Based Siamese Network for Change Detection","date":"2022-01-04","rows_on_this_dataset":2,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":4,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/change-is-everywhere-single-temporal","title":"Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery","date":"2021-08-16","rows_on_this_dataset":6,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":0,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/adversarial-instance-augmentation-for","title":"Adversarial Instance Augmentation for Building Change Detection in Remote Sensing Images","date":"2021-03-25","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/efficient-transformer-based-method-for-remote","title":"Remote Sensing Image Change Detection with Transformers","date":"2021-02-27","rows_on_this_dataset":1,"code_links":5,"syntology":null},{"paper":"/paper/looking-for-change-roll-the-dice-and-demand","title":"Looking for change? Roll the Dice and demand Attention","date":"2020-09-04","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/a-spatial-temporal-attention-based-method-and","title":"A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection","date":"2020-05-22","rows_on_this_dataset":1,"code_links":5,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":5,"samples_harvested":33,"samples_ran":20,"samples_unverified":13,"pointer_only_for_licence":6,"papers_with_no_sample_that_ran":1,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}