mlsanityData to 7 Oct 2026

Cloud-Net: An end-to-end Cloud Detection Algorithm for Landsat 8 Imagery

arXiv 1901.10077 ↗First posted 29 Jan 2019cs.CVcited by 25

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Abstract (arXiv)

Cloud detection in satellite images is an important first-step in many remote sensing applications. This problem is more challenging when only a limited number of spectral bands are available. To address this problem, a deep learning-based algorithm is proposed in this paper. This algorithm consists of a Fully Convolutional Network (FCN) that is trained by multiple patches of Landsat 8 images. This network, which is called Cloud-Net, is capable of capturing global and local cloud features in an image using its convolutional blocks. Since the proposed method is an end-to-end solution, no complicated pre-processing step is required. Our experimental results prove that the proposed method outperforms the state-of-the-art method over a benchmark dataset by 8.7% in Jaccard Index.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Jan 2019.

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Citing papers per year: 2021: 1; 2022: 2; 2023: 5; 2024: 11; 2025: 3; 2026: 3.

25 papers.

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  1. 2024PANGAEA: A Global and Inclusive Benchmark for Geospatial Foundation Modelscited by 62
  2. 2023Segment anything, from space?cited by 42
  3. 2022EarthNets: Empowering AI in Earth Observationcited by 40
  4. 2023Detecting Clouds in Multispectral Satellite Images Using Quantum-Kernel Support Vector Machinescited by 15
  5. 2024Diffusion Enhancement for Cloud Removal in Ultra-Resolution Remote Sensing Imagerycited by 15
  6. 2024RSAM-Seg: A SAM-based Approach with Prior Knowledge Integration for Remote Sensing Image Semantic Segmentationcited by 9
  7. 2024Panoptic Perception: A Novel Task and Fine-grained Dataset for Universal Remote Sensing Image Interpretationcited by 8
  8. 2024High-Resolution Cloud Detection Networkcited by 8
  9. 2025ChA-MAEViT: Unifying Channel-Aware Masked Autoencoders and Multi-Channel Vision Transformers for Improved Cross-Channel Learningcited by 6
  10. 2021SEnSeI: A Deep Learning Module for Creating Sensor Independent Cloud Maskscited by 4
  11. 2024Machine Learning in Space: Surveying the Robustness of on-board ML models to Radiationcited by 3
  12. 2022Self-Configuring nnU-Nets Detect Clouds in Satellite Imagescited by 2
13 more
  1. 2023Squeezing nnU-Nets with Knowledge Distillation for On-Board Cloud Detectioncited by 2
  2. 2024BenchCloudVision: A Benchmark Analysis of Deep Learning Approaches for Cloud Detection and Segmentation in Remote Sensing Imagerycited by 2
  3. 2025FPGA-Based Neural Network Accelerators for Space Applications: A Surveycited by 2
  4. 2025FPGA-Enabled Machine Learning Applications in Earth Observation: A Systematic Reviewcited by 2
  5. 2023Intriguing Property and Counterfactual Explanation of GAN for Remote Sensing Image Generationcited by 1
  6. 2024MT-HCCAR: Multi-Task Deep Learning with Hierarchical Classification and Attention-based Regression for Cloud Property Retrievalcited by 1
  7. 2024MLMT-CNN for Object Detection and Segmentation in Multi-layer and Multi-spectral Imagescited by 1
  8. 2023Optimizing Kernel-Target Alignment for cloud detection in multispectral satellite imagescited by 0
  9. 2024Distribution-aware Interactive Attention Network and Large-scale Cloud Recognition Benchmark on FY-4A Satellite Imagecited by 0
  10. 2024MCSDNet: Mesoscale Convective System Detection Network via Multi-scale Spatiotemporal Informationcited by 0
  11. 2026TIRAuxCloud: A Thermal Infrared Dataset for Day and Night Cloud Detectioncited by 0
  12. 2026Uncertainty-Guided Edge Learning for Deep Image Regression in Remote Sensingcited by 0
  13. 2026GeoSAM-Lite: A Lightweight Foundation Model for Onboard Remote Sensing Segmentationcited by 0