# AllClear: A Comprehensive Dataset and Benchmark for Cloud Removal in Satellite Imagery

arXiv: https://arxiv.org/abs/2410.23891 · HTML: https://mlsanity.com/paper/2410.23891/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 44 references that are in the map.
- First posted on arXiv 31 Oct 2024.

## Builds on (most cited first, 12 of 12)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [DiffusionSat: A Generative Foundation Model for Satellite Imagery](https://mlsanity.com/paper/2312.03606/) (2023)
- [Multi-Sensor Data Fusion for Cloud Removal in Global and All-Season Sentinel-2 Imagery](https://mlsanity.com/paper/2009.07683/) (2020)
- [EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task](https://mlsanity.com/paper/2104.10066/) (2021)
- [SEN12MS-CR-TS: A Remote Sensing Data Set for Multi-modal Multi-temporal Cloud Removal](https://mlsanity.com/paper/2201.09613/) (2022)
- [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)
- [Filmy Cloud Removal on Satellite Imagery with Multispectral Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1710.04835/) (2017)
- [PMAA: A Progressive Multi-scale Attention Autoencoder Model for High-performance Cloud Removal from Multi-temporal Satellite Imagery](https://mlsanity.com/paper/2303.16565/) (2023)
- [Seeing Through Clouds in Satellite Images](https://mlsanity.com/paper/2106.08408/) (2021)
- [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)

## Built on by (most cited first, 7 of 7)

- [UniTS: Unified Spatio-Temporal Generative Model for Remote Sensing](https://mlsanity.com/paper/2512.04461/) (2025)
- [TerraCodec: Compressing Optical Earth Observation Data](https://mlsanity.com/paper/2510.12670/) (2025)
- [Learning Multimodal Embeddings for Traffic Accident Prediction and Causal Estimation](https://mlsanity.com/paper/2512.02920/) (2025)
- [TIRAuxCloud: A Thermal Infrared Dataset for Day and Night Cloud Detection](https://mlsanity.com/paper/2602.21905/) (2026)
- [Lean Learning Beyond Clouds: Efficient Discrepancy-Conditioned Optical-SAR Fusion for Semantic Segmentation](https://mlsanity.com/paper/2603.20811/) (2026)
- [Clear-Weighted Bit Allocation for Satellite Downlinks](https://mlsanity.com/paper/2608.01457/) (2026)
- [GeoCR: Learning a Generalist Cloud Removal Prior from Heterogeneous Observations](https://mlsanity.com/paper/2609.32510/) (2026)

## Abstract (arXiv)

Clouds in satellite imagery pose a significant challenge for downstream applications. A major challenge in current cloud removal research is the absence of a comprehensive benchmark and a sufficiently large and diverse training dataset. To address this problem, we introduce the largest public dataset -- textitAllClear for cloud removal, featuring 23,742 globally distributed regions of interest (ROIs) with diverse land-use patterns, comprising 4 million images in total. Each ROI includes complete temporal captures from the year 2022, with (1) multi-spectral optical imagery from Sentinel-2 and Landsat 8/9, (2) synthetic aperture radar (SAR) imagery from Sentinel-1, and (3) auxiliary remote sensing products such as cloud masks and land cover maps. We validate the effectiveness of our dataset by benchmarking performance, demonstrating the scaling law -- the PSNR rises from 28.47 to 33.87 with 30× more data, and conducting ablation studies on the temporal length and the importance of individual modalities. This dataset aims to provide comprehensive coverage of the Earth's surface and promote better cloud removal results.