{"url":"/dataset/dsifn-cd","name":"DSIFN-CD","full_name":null,"description_markdown":"The dataset is manually collected from Google Earth. It consists of six large bi-temporal high resolution images covering six cities (i.e., Beijing, Chengdu, Shenzhen, Chongqing, Wuhan, Xian) in China. The five large image-pairs (i.e., Beijing, Chengdu, Shenzhen, Chongqing, Wuhan) are clipped into 394 subimage pairs with sizes of 512×512. After data augmentation, a collection of 3940 bi-temporal image pairs is acquired. Xian image pair is clipped into 48 image pairs for model testing. There are 3600 image pairs in the training dataset, 340 image paris in the validation dataset, and 48 image pairs in the test dataset.","description_withheld":null,"homepage":"https://github.com/GeoZcx/A-deeply-supervised-image-fusion-network-for-change-detection-in-remote-sensing-images/tree/master/dataset","introduced_date":"2020-06-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/a-deeply-supervised-image-fusion-network-for","title":"A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images","first_author":"Zhang","url":null},"license":null,"modalities":[],"tasks":[{"name":"Change Detection","url":"/task/change-detection","datasets_with_task":"/datasets/task/change-detection"}],"languages":[],"variants":["DSIFN-CD"],"data_loaders":[],"num_papers_in_archive":29,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/change-detection-on-dsifn-cd","task":"Change Detection","dataset_variant":"DSIFN-CD","rows":9,"metrics":["F1","Precision","Recall","Overall Accuracy","KC","IoU","Params(M)"],"first_row_in_archive_order":{"model":"DDPM-CD","paper":"/paper/remote-sensing-change-detection-segmentation","metrics":{"F1":"96.65","Overall Accuracy":"97.09"},"code_links":[{"title":"wgcban/ddpm-cd","url":"https://github.com/wgcban/ddpm-cd"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"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/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/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/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/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."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":13,"samples_ran":10,"samples_unverified":3,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"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."}