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Consequently, persistent cloud coverage affects the remote sensing practitioner's capabilities of a continuous and seamless monitoring of our planet. **Cloud removal** is the task of reconstructing cloud-covered information while preserving originally cloud-free details.\r\n\r\nImage Source: [URL](https://patrickTUM.github.io/cloud_removal/)","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["RMSE","PSNR","SSIM","SAM"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"RMSE":"lower","PSNR":"higher","SSIM":"higher","SAM":null}},"counts":{"rows":7,"rows_with_code":6,"rows_with_paper_page":7,"rows_dated":7,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"SeqDMs","metrics":{"PSNR":"28.07","RMSE":"0.045","SAM":"12.777","SSIM":"0.827"},"uses_additional_data":false,"paper_date":"2023-05-31","paper":"/paper/cloud-removal-in-remote-sensing-using","paper_url":"https://www.mdpi.com/2072-4292/15/11/2861","paper_title":"Cloud Removal in Remote Sensing Using Sequential-Based Diffusion Models","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":2,"model":"UnCRtainTS L2","metrics":{"PSNR":"27.23","RMSE":"0.049","SAM":"10.168","SSIM":"0.859"},"uses_additional_data":false,"paper_date":"2023-04-11","paper":"/paper/uncrtaints-uncertainty-quantification-for","paper_url":"https://arxiv.org/abs/2304.05464v1","paper_title":"UnCRtainTS: Uncertainty Quantification for Cloud Removal in Optical Satellite Time Series","code":"https://github.com/PatrickTUM/UnCRtainTS","n_code_links":1,"syntology":null},{"rank_in_archive_order":3,"model":"UnCRtainTS σ","metrics":{"PSNR":"27.84","RMSE":"0.051","SAM":"10.160","SSIM":"0.866"},"uses_additional_data":false,"paper_date":"2023-04-11","paper":"/paper/uncrtaints-uncertainty-quantification-for","paper_url":"https://arxiv.org/abs/2304.05464v1","paper_title":"UnCRtainTS: Uncertainty Quantification for Cloud Removal in Optical Satellite Time Series","code":"https://github.com/PatrickTUM/UnCRtainTS","n_code_links":1,"syntology":null},{"rank_in_archive_order":4,"model":"U-TAE","metrics":{"PSNR":"27.05","RMSE":"0.051","SAM":"11.649","SSIM":"0.849"},"uses_additional_data":false,"paper_date":"2021-07-16","paper":"/paper/panoptic-segmentation-of-satellite-image-time","paper_url":"https://arxiv.org/abs/2107.07933v4","paper_title":"Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks","code":"https://github.com/VSainteuf/utae-paps","n_code_links":1,"syntology":{"n_ran":10,"n_unverified":1,"n_samples":11,"n_pointer_only_licence":0}},{"rank_in_archive_order":5,"model":"CR-TS Net","metrics":{"PSNR":"26.68","RMSE":"0.051","SAM":"10.657","SSIM":"0.836"},"uses_additional_data":false,"paper_date":"2022-01-24","paper":"/paper/sen12ms-cr-ts-a-remote-sensing-data-set-for","paper_url":"https://arxiv.org/abs/2201.09613v1","paper_title":"SEN12MS-CR-TS: A Remote Sensing Data Set for Multi-modal Multi-temporal Cloud Removal","code":"https://github.com/PatrickTUM/SEN12MS-CR-TS","n_code_links":1,"syntology":null},{"rank_in_archive_order":6,"model":"STGAN","metrics":{"PSNR":"25.42","RMSE":"0.057","SAM":"12.548","SSIM":"0.818"},"uses_additional_data":false,"paper_date":"2019-12-14","paper":"/paper/cloud-removal-in-satellite-images-using","paper_url":"https://arxiv.org/abs/1912.06838v1","paper_title":"Cloud Removal in Satellite Images Using Spatiotemporal Generative Networks","code":"https://github.com/PatrickTUM/SEN12MS-CR-TS","n_code_links":3,"syntology":null},{"rank_in_archive_order":7,"model":"DSen2-CR","metrics":{"PSNR":"26.04","RMSE":"0.060","SAM":"12.147","SSIM":"0.810"},"uses_additional_data":false,"paper_date":"2020-07-02","paper":"/paper/cloud-removal-in-sentinel-2-imagery-using-a","paper_url":"https://www.sciencedirect.com/science/article/pii/S0924271620301398","paper_title":"Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion","code":"https://github.com/ameraner/dsen2-cr","n_code_links":1,"syntology":null}],"since_archive":{"claim":"Results that newer papers report for their own method, placed here by Syntology. 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