{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/sen12ms-cr-ts-a-remote-sensing-data-set-for","title":"SEN12MS-CR-TS: A Remote Sensing Data Set for Multi-modal Multi-temporal Cloud Removal","arxiv_id":"2201.09613","date":"2022-01-24","proceeding":null,"authors":["Patrick Ebel","Yajin Xu","Michael Schmitt","Xiaoxiang Zhu"],"abstract":"About half of all optical observations collected via spaceborne satellites are affected by haze or clouds. Consequently, cloud coverage affects the remote sensing practitioner's capabilities of a continuous and seamless monitoring of our planet. This work addresses the challenge of optical satellite image reconstruction and cloud removal by proposing a novel multi-modal and multi-temporal data set called SEN12MS-CR-TS. We propose two models highlighting the benefits and use cases of SEN12MS-CR-TS: First, a multi-modal multi-temporal 3D-Convolution Neural Network that predicts a cloud-free image from a sequence of cloudy optical and radar images. Second, a sequence-to-sequence translation model that predicts a cloud-free time series from a cloud-covered time series. Both approaches are evaluated experimentally, with their respective models trained and tested on SEN12MS-CR-TS. The conducted experiments highlight the contribution of our data set to the remote sensing community as well as the benefits of multi-modal and multi-temporal information to reconstruct noisy information. Our data set is available at https://patrickTUM.github.io/cloud_removal","url_abs":"https://arxiv.org/abs/2201.09613v1","url_pdf":"https://arxiv.org/pdf/2201.09613v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"sen12ms-cr-ts-a-remote-sensing-data-set-for","repo_url":"https://github.com/PatrickTUM/SEN12MS-CR-TS","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"cloud-removal","task_name":"Cloud Removal"},{"task_slug":"image-reconstruction","task_name":"Image Reconstruction"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[{"slug":"sen12ms-cr-ts","name":"SEN12MS-CR-TS","full_name":"SEN12MS-CR-TS"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/cloud-removal-on-sen12ms-cr-ts","task":"Cloud Removal","dataset":"SEN12MS-CR-TS","model":"CR-TS Net","rank_in_archive_order":5,"of":7,"metrics":{"PSNR":"26.68","RMSE":"0.051","SAM":"10.657","SSIM":"0.836"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2201.09613","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}