Cloud Removal

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8 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

GeoCR: Learning a Generalist Cloud Removal Prior from Heterogeneous Observations
Sep 26, 2026Jeonghyeok Do, Munchurl KimCloud Removal Remote Sensing
Korea Advanced Institute of Science and Technology (KAIST)
ORBITALIF: An Efficient Spiking Federated Learning Framework for Onboard Cloud Removal
Aug 25, 2026Bohan Zhang, Chenyu Xu, Yijie Mao +1Cloud Removal Remote Sensing
School of Information Science and Technology, ShanghaiTech University, Shanghai, China
Global-Scale Self-Supervised Spatiotemporal Learning for NDVI Time-Series Reconstruction
Aug 3, 2026Ang Li, Menghui Jiang, Xiaobin Guan +2Time Series Imputation Self-Supervised Learning
School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China · School of Geography and Tourism, Anhui Normal University, Wuhu 241002, China · Key Laboratory of Earth Surface Processes and Regional Response in the Yangtze River Basin, Wuhu 241002, China +2
Interpretation-Oriented Cloud Removal via Observation-Anchored Residual Flow with Geo-Contextual Alignment
Jul 2, 2026Ziyao Wang, Maonan Wang, Yucheng He +5Residual Learning Image Restoration
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Shenzhen, China · Shanghai Ai Lab, Shanghai, China · Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu,2026 China
ATT-CR: Adaptive Triangular Transformer for Cloud Removal
Jun 4, 2026Yang Wu, Ye Deng, Pengna Li +4Remote Sensing Transformer Attention
Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China · School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu, Sichuan 611130, China · Ningbo University of Technology, Ningbo, Zhejiang 315211, China
Deep Learning for Remote Sensing to Improve Flood Inundation Mapping
Jun 1, 2026Yogesh Bhattarai, Vijay Chaudhary, Wai Lim Kim +1Diffusion Models for Image Restoration Flood Inundation Mapping
Department of Civil and Environmental Engineering Howard University Washington, D.C, 20060, USA. · Department of Computer Science and Electrical Engineering Howard University Washington, D.C, 20060, USA. · Department of Medicine Howard University Washington, D.C, 20060, USA.
Asynchronous Remote Sensing Time-Series Fusion for Cloud Removal and Anytime Reconstruction
May 26, 2026Forouzan Fallah, Chia Yu Hsu, Wenwen Li +2Flow Matching Remote Sensing
School of Computing and Augmented Intelligence, Arizona State University · School of Geographical Sciences and Urban Planning, Arizona State University · Woodwell Climate Research Center
Lean Learning Beyond Clouds: Efficient Discrepancy-Conditioned Optical-SAR Fusion for Semantic Segmentation
Mar 21, 2026Chenxing Meng, Wuzhou Quan, Yingjie Cai +3Efficient Semantic Segmentation Cloud Removal
School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

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GeoCR: Learning a Generalist Cloud Removal Prior from Heterogeneous Observations

Jeonghyeok Do, Munchurl Kim

Korea Advanced Institute of Science and Technology (KAIST)

Cloud Removal Remote Sensing Image Restoration All-in-One Image Restoration

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Cloud removal methods are typically specialized to individual datasets and input configurations, limiting reuse across sensors, spectral bands, and observation settings. We introduce GeoCR, a generalist model that unifies RGB-only-based CR and multispectral-based CR from single- or multi-temporal cloudy observations, with optional SAR guidance, within a single network. To accommodate different spectral and sensing domains, compact input and output stems extend a pretrained RGB autoencoder while keeping its encoder and decoder trunks frozen. This shared latent interface enables a single flow transformer to jointly model clean RGB and non-RGB latents, conditioned on separate cloudy-observation streams and optional SAR tokens. Through joint pretraining on the training splits of ten datasets comprising 883,331 cloud-free target images, GeoCR learns a shared cloud removal prior across these heterogeneous configurations. The same pretrained checkpoint supports direct inference without dataset-specific fine-tuning and efficient adaptation through low-rank adaptation (LoRA). We evaluate GeoCR against general image restoration and cloud removal methods on test splits of the contributing datasets under full-band and RGB-only settings. GeoCR achieves the best FID and DISTS on full-band SEN12MS-CR and Sen2_MTC_New and RGB-only CUHK-CR2, outperforming existing models and demonstrating the effectiveness of a reusable generative model across diverse settings.