mlsanityData to 14 Oct 2026

SEN12MS-CR-TS: A Remote Sensing Data Set for Multi-modal Multi-temporal Cloud Removal

arXiv 2201.09613 ↗First posted 24 Jan 2022cs.CV eess.IVcited by 32

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Abstract (arXiv)

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

Key facts. Cited by 32 papers in the map (data through 14 Oct 2026). Builds on 14 of its 57 references that are in the map. First posted on arXiv 24 Jan 2022.

Builds on

14 of 57 references.

References · 14

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
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  7. 2017Deep Image Priorcited by 1,098
  8. 2019SEN12MS -- A Curated Dataset of Georeferenced Multi-Spectral Sentinel-1/2 Imagery for Deep Learning and Data Fusioncited by 95
  9. 2020Multi-Sensor Data Fusion for Cloud Removal in Global and All-Season Sentinel-2 Imagerycited by 36
  10. 2019An Internal Learning Approach to Video Inpaintingcited by 33
  11. 2017Filmy Cloud Removal on Satellite Imagery with Multispectral Conditional Generative Adversarial Netscited by 26
  12. 2021Self-Supervised Multisensor Change Detectioncited by 19
2 more
  1. 2020Weakly Supervised Semantic Segmentation of Satellite Images for Land Cover Mapping -- Challenges and Opportunitiescited by 15
  2. 2021Remote Sensing Image Classification with the SEN12MS Datasetcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 3; 2023: 6; 2024: 8; 2025: 6; 2026: 9.

32 papers.

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  1. 2024OmniSat: Self-Supervised Modality Fusion for Earth Observationcited by 63
  2. 2024Major TOM: Expandable Datasets for Earth Observationcited by 32
  3. 2023UnCRtainTS: Uncertainty Quantification for Cloud Removal in Optical Satellite Time Seriescited by 28
  4. 2023DiffCR: A Fast Conditional Diffusion Framework for Cloud Removal from Optical Satellite Imagescited by 27
  5. 2022GLF-CR: SAR-Enhanced Cloud Removal with Global-Local Fusioncited by 24
  6. 2022AI Security for Geoscience and Remote Sensing: Challenges and Future Trendscited by 23
  7. 2023Better, Not Just More: Data-Centric Machine Learning for Earth Observationcited by 19
  8. 2024Diffusion Enhancement for Cloud Removal in Ultra-Resolution Remote Sensing Imagerycited by 16
  9. 2024IDF-CR: Iterative Diffusion Process for Divide-and-Conquer Cloud Removal in Remote-sensing Imagescited by 13
  10. 2025Effective Cloud Removal for Remote Sensing Images by an Improved Mean-Reverting Denoising Model with Elucidated Design Spacecited by 12
  11. 2023Exploring the Capability of Text-to-Image Diffusion Models with Structural Edge Guidance for Multi-Spectral Satellite Image Inpaintingcited by 9
  12. 2023U-TILISE: A Sequence-to-sequence Model for Cloud Removal in Optical Satellite Time Seriescited by 8
20 more
  1. 2023Multi-Modal and Multi-Resolution Data Fusion for High-Resolution Cloud Removal: A Novel Baseline and Benchmarkcited by 7
  2. 2024AllClear: A Comprehensive Dataset and Benchmark for Cloud Removal in Satellite Imagerycited by 7
  3. 2024Fus-MAE: A cross-attention-based data fusion approach for Masked Autoencoders in remote sensingcited by 6
  4. 2022Common Practices and Taxonomy in Deep Multi-view Fusion for Remote Sensing Applicationscited by 5
  5. 2024Attentive Contextual Attention for Cloud Removalcited by 5
  6. 2025DC4CR: When Cloud Removal Meets Diffusion Control in Remote Sensingcited by 4
  7. 2025Core-Set Selection for Data-efficient Land Cover Segmentationcited by 3
  8. 2024Detecting Looted Archaeological Sites from Satellite Image Time Seriescited by 2
  9. 2025UniTS: Unified Spatio-Temporal Generative Model for Remote Sensingcited by 2
  10. 2025Benchmarking for Practice: Few-Shot Time-Series Crop-Type Classification on the EuroCropsML Datasetcited by 1
  11. 2026SADER: Structure-Aware Diffusion Framework with DEterministic Resampling for Multi-Temporal Remote Sensing Cloud Removalcited by 1
  12. 2026CBEN -- A Multimodal Machine Learning Dataset for Cloud Robust Remote Sensing Image Understandingcited by 1
  13. 2026Interpretation-Oriented Cloud Removal via Observation-Anchored Residual Flow with Geo-Contextual Alignmentcited by 1
  14. 2026Difference-Driven Gating: Adaptive Feature Fusion for U-Net Decodercited by 1
  15. 2025DehazeMamba: SAR-guided Optical Remote Sensing Image Dehazing with Adaptive State Space Modelcited by 0
  16. 2026Lean Learning Beyond Clouds: Efficient Discrepancy-Conditioned Optical-SAR Fusion for Semantic Segmentationcited by 0
  17. 2026Densification and forecasting of Sentinel-2 time series from multimodal SAR and Optical satellite data using deep generative modelscited by 0
  18. 2026IB-HFN: Information Bottleneck-Driven SAR-Optical Fusion Network for High-Fidelity Cloud Removalcited by 0
  19. 2026From Surface Forecasting to Observability Forecasting: A Latent World Model for Cloud-Aware EO Monitoringcited by 0
  20. 2026GeoCore-9B: Towards Geo-Aware Generative Foundation Models in Earth Observationcited by 0