# Multi-Sensor Data Fusion for Cloud Removal in Global and All-Season Sentinel-2 Imagery

arXiv: https://arxiv.org/abs/2009.07683 · HTML: https://mlsanity.com/paper/2009.07683/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2020.

## Built on by (most cited first, 30 of 36)

- [Bridging Remote Sensors with Multisensor Geospatial Foundation Models](https://mlsanity.com/paper/2404.01260/) (2024)
- [EarthNets: Empowering AI in Earth Observation](https://mlsanity.com/paper/2210.04936/) (2022)
- [SEN12MS-CR-TS: A Remote Sensing Data Set for Multi-modal Multi-temporal Cloud Removal](https://mlsanity.com/paper/2201.09613/) (2022)
- [Major TOM: Expandable Datasets for Earth Observation](https://mlsanity.com/paper/2402.12095/) (2024)
- [UnCRtainTS: Uncertainty Quantification for Cloud Removal in Optical Satellite Time Series](https://mlsanity.com/paper/2304.05464/) (2023)
- [DiffCR: A Fast Conditional Diffusion Framework for Cloud Removal from Optical Satellite Images](https://mlsanity.com/paper/2308.04417/) (2023)
- [GLF-CR: SAR-Enhanced Cloud Removal with Global-Local Fusion](https://mlsanity.com/paper/2206.02850/) (2022)
- [REOBench: Benchmarking Robustness of Earth Observation Foundation Models](https://mlsanity.com/paper/2505.16793/) (2025)
- [Effective Cloud Removal for Remote Sensing Images by an Improved Mean-Reverting Denoising Model with Elucidated Design Space](https://mlsanity.com/paper/2503.23717/) (2025)
- [U-TILISE: A Sequence-to-sequence Model for Cloud Removal in Optical Satellite Time Series](https://mlsanity.com/paper/2305.13277/) (2023)
- [Multi-Modal and Multi-Resolution Data Fusion for High-Resolution Cloud Removal: A Novel Baseline and Benchmark](https://mlsanity.com/paper/2301.03432/) (2023)
- [AllClear: A Comprehensive Dataset and Benchmark for Cloud Removal in Satellite Imagery](https://mlsanity.com/paper/2410.23891/) (2024)
- [Generative modeling of spatio-temporal weather patterns with extreme event conditioning](https://mlsanity.com/paper/2104.12469/) (2021)
- [S4: Self-Supervised Sensing Across the Spectrum](https://mlsanity.com/paper/2405.01656/) (2024)
- [Attentive Contextual Attention for Cloud Removal](https://mlsanity.com/paper/2411.13042/) (2024)
- [Text-Guided Coarse-to-Fine Fusion Network for Robust Remote Sensing Visual Question Answering](https://mlsanity.com/paper/2411.15770/) (2024)
- [SOMA-1M: A Large-Scale SAR-Optical Multi-resolution Alignment Dataset for Multi-Task Remote Sensing](https://mlsanity.com/paper/2602.05480/) (2026)
- [A Unified Foundation Model for All-in-One Multi-Modal Remote Sensing Image Restoration and Fusion with Language Prompting](https://mlsanity.com/paper/2604.05629/) (2026)
- [An Integrated Framework for the Heterogeneous Spatio-Spectral-Temporal Fusion of Remote Sensing Images](https://mlsanity.com/paper/2109.00400/) (2021)
- [A Benchmarking Protocol for SAR Colorization: From Regression to Deep Learning Approaches](https://mlsanity.com/paper/2310.08705/) (2023)
- [SAR-to-RGB Translation with Latent Diffusion for Earth Observation](https://mlsanity.com/paper/2504.11154/) (2025)
- [Cloud-Aware SAR Fusion for Enhanced Optical Sensing in Space Missions](https://mlsanity.com/paper/2506.17885/) (2025)
- [CloudBreaker: Breaking the Cloud Covers of Sentinel-2 Images using Multi-Stage Trained Conditional Flow Matching on Sentinel-1](https://mlsanity.com/paper/2508.03608/) (2025)
- [CBEN -- A Multimodal Machine Learning Dataset for Cloud Robust Remote Sensing Image Understanding](https://mlsanity.com/paper/2602.12652/) (2026)
- [ATT-CR: Adaptive Triangular Transformer for Cloud Removal](https://mlsanity.com/paper/2606.05999/) (2026)
- [Spatio-Temporal SAR-Optical Data Fusion for Cloud Removal via a Deep Hierarchical Model](https://mlsanity.com/paper/2106.12226/) (2021)
- [Comparison of convolutional neural networks for cloudy optical images reconstruction from single or multitemporal joint SAR and optical images](https://mlsanity.com/paper/2204.00424/) (2022)
- [FSID: Fully Synthetic Image Denoising via Procedural Scene Generation](https://mlsanity.com/paper/2212.03961/) (2022)
- [MM811 Project Report: Cloud Detection and Removal in Satellite Images](https://mlsanity.com/paper/2212.11369/) (2022)
- [DehazeMamba: SAR-guided Optical Remote Sensing Image Dehazing with Adaptive State Space Model](https://mlsanity.com/paper/2503.13073/) (2025)

## Abstract (arXiv)

This work has been accepted by IEEE TGRS for publication. The majority of optical observations acquired via spaceborne earth imagery are affected by clouds. While there is numerous prior work on reconstructing cloud-covered information, previous studies are oftentimes confined to narrowly-defined regions of interest, raising the question of whether an approach can generalize to a diverse set of observations acquired at variable cloud coverage or in different regions and seasons. We target the challenge of generalization by curating a large novel data set for training new cloud removal approaches and evaluate on two recently proposed performance metrics of image quality and diversity. Our data set is the first publically available to contain a global sample of co-registered radar and optical observations, cloudy as well as cloud-free. Based on the observation that cloud coverage varies widely between clear skies and absolute coverage, we propose a novel model that can deal with either extremes and evaluate its performance on our proposed data set. Finally, we demonstrate the superiority of training models on real over synthetic data, underlining the need for a carefully curated data set of real observations. To facilitate future research, our data set is made available online