# A Remote Sensing Image Dataset for Cloud Removal

arXiv: https://arxiv.org/abs/1901.00600 · HTML: https://mlsanity.com/paper/1901.00600/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jan 2019.

## Built on by (most cited first, 29 of 29)

- [DiffCR: A Fast Conditional Diffusion Framework for Cloud Removal from Optical Satellite Images](https://mlsanity.com/paper/2308.04417/) (2023)
- [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)
- [Diffusion Enhancement for Cloud Removal in Ultra-Resolution Remote Sensing Imagery](https://mlsanity.com/paper/2401.15105/) (2024)
- [IDF-CR: Iterative Diffusion Process for Divide-and-Conquer Cloud Removal in Remote-sensing Images](https://mlsanity.com/paper/2403.11870/) (2024)
- [Real-World Remote Sensing Image Dehazing: Benchmark and Baseline](https://mlsanity.com/paper/2503.17966/) (2025)
- [Disentangling Noise from Images: A Flow-Based Image Denoising Neural Network](https://mlsanity.com/paper/2105.04746/) (2021)
- [Multi-Modal and Multi-Resolution Data Fusion for High-Resolution Cloud Removal: A Novel Baseline and Benchmark](https://mlsanity.com/paper/2301.03432/) (2023)
- [Learning Domain-Aware Task Prompt Representations for Multi-Domain All-in-One Image Restoration](https://mlsanity.com/paper/2603.01725/) (2026)
- [RSHazeDiff: A Unified Fourier-aware Diffusion Model for Remote Sensing Image Dehazing](https://mlsanity.com/paper/2405.09083/) (2024)
- [Coarse-to-fine Task-driven Inpainting for Geoscience Images](https://mlsanity.com/paper/2211.11059/) (2022)
- [Attentive Contextual Attention for Cloud Removal](https://mlsanity.com/paper/2411.13042/) (2024)
- [DC4CR: When Cloud Removal Meets Diffusion Control in Remote Sensing](https://mlsanity.com/paper/2504.14785/) (2025)
- [Unveiling the Invisible: Reasoning Complex Occlusions Amodally with AURA](https://mlsanity.com/paper/2503.10225/) (2025)
- [MST: Adaptive Multi-Scale Tokens Guided Interactive Segmentation](https://mlsanity.com/paper/2401.04403/) (2024)
- [Structured Click Control in Transformer-based Interactive Segmentation](https://mlsanity.com/paper/2405.04009/) (2024)
- [Dehazing Remote Sensing and UAV Imagery: A Review of Deep Learning, Prior-based, and Hybrid Approaches](https://mlsanity.com/paper/2405.07520/) (2024)
- [Patch-GAN Transfer Learning with Reconstructive Models for Cloud Removal](https://mlsanity.com/paper/2501.05265/) (2025)
- [Task-Guided Prompting for Unified Remote Sensing Image Restoration](https://mlsanity.com/paper/2604.02742/) (2026)
- [Enhancing Satellite Imagery using Deep Learning for the Sensor To Shooter Timeline](https://mlsanity.com/paper/2203.00116/) (2022)
- [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)
- [Cloud Removal from Satellite Images](https://mlsanity.com/paper/2112.15483/) (2021)
- [MM811 Project Report: Cloud Detection and Removal in Satellite Images](https://mlsanity.com/paper/2212.11369/) (2022)
- [Fine-Tuning Adversarially-Robust Transformers for Single-Image Dehazing](https://mlsanity.com/paper/2504.17829/) (2025)
- [Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss](https://mlsanity.com/paper/2509.00835/) (2025)
- [CloudMatch: Weak-to-Strong Consistency Learning for Semi-Supervised Cloud Detection](https://mlsanity.com/paper/2601.03528/) (2026)
- [DPSF-Net: A Dual-Prior Spatial-Frequency Network for Real-World Remote Sensing Image Dehazing](https://mlsanity.com/paper/2609.06962/) (2026)
- [GeoCR: Learning a Generalist Cloud Removal Prior from Heterogeneous Observations](https://mlsanity.com/paper/2609.32510/) (2026)
- [EM-SNN: Efficiently Modulated Spiking Neural Network for Remote Sensing Image Dehazing](https://mlsanity.com/paper/2610.09275/) (2026)

## Abstract (arXiv)

Cloud-based overlays are often present in optical remote sensing images, thus limiting the application of acquired data. Removing clouds is an indispensable pre-processing step in remote sensing image analysis. Deep learning has achieved great success in the field of remote sensing in recent years, including scene classification and change detection. However, deep learning is rarely applied in remote sensing image removal clouds. The reason is the lack of data sets for training neural networks. In order to solve this problem, this paper first proposed the Remote sensing Image Cloud rEmoving dataset (RICE). The proposed dataset consists of two parts: RICE1 contains 500 pairs of images, each pair has images with cloud and cloudless size of 512*512; RICE2 contains 450 sets of images, each set contains three 512*512 size images. , respectively, the reference picture without clouds, the picture of the cloud and the mask of its cloud. The dataset is freely available at urlhttps://github.com/BUPTLdy/RICEDATASET.