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

arXiv: https://arxiv.org/abs/1901.10077 · HTML: https://mlsanity.com/paper/1901.10077/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2019.

## Built on by (most cited first, 25 of 25)

- [PANGAEA: A Global and Inclusive Benchmark for Geospatial Foundation Models](https://mlsanity.com/paper/2412.04204/) (2024)
- [Segment anything, from space?](https://mlsanity.com/paper/2304.13000/) (2023)
- [EarthNets: Empowering AI in Earth Observation](https://mlsanity.com/paper/2210.04936/) (2022)
- [Diffusion Enhancement for Cloud Removal in Ultra-Resolution Remote Sensing Imagery](https://mlsanity.com/paper/2401.15105/) (2024)
- [Detecting Clouds in Multispectral Satellite Images Using Quantum-Kernel Support Vector Machines](https://mlsanity.com/paper/2302.08270/) (2023)
- [RSAM-Seg: A SAM-based Approach with Prior Knowledge Integration for Remote Sensing Image Semantic Segmentation](https://mlsanity.com/paper/2402.19004/) (2024)
- [Panoptic Perception: A Novel Task and Fine-grained Dataset for Universal Remote Sensing Image Interpretation](https://mlsanity.com/paper/2404.04608/) (2024)
- [High-Resolution Cloud Detection Network](https://mlsanity.com/paper/2407.07365/) (2024)
- [ChA-MAEViT: Unifying Channel-Aware Masked Autoencoders and Multi-Channel Vision Transformers for Improved Cross-Channel Learning](https://mlsanity.com/paper/2503.19331/) (2025)
- [SEnSeI: A Deep Learning Module for Creating Sensor Independent Cloud Masks](https://mlsanity.com/paper/2111.08349/) (2021)
- [Machine Learning in Space: Surveying the Robustness of on-board ML models to Radiation](https://mlsanity.com/paper/2405.02642/) (2024)
- [Self-Configuring nnU-Nets Detect Clouds in Satellite Images](https://mlsanity.com/paper/2210.13659/) (2022)
- [Squeezing nnU-Nets with Knowledge Distillation for On-Board Cloud Detection](https://mlsanity.com/paper/2306.09886/) (2023)
- [BenchCloudVision: A Benchmark Analysis of Deep Learning Approaches for Cloud Detection and Segmentation in Remote Sensing Imagery](https://mlsanity.com/paper/2402.13918/) (2024)
- [FPGA-Based Neural Network Accelerators for Space Applications: A Survey](https://mlsanity.com/paper/2504.16173/) (2025)
- [FPGA-Enabled Machine Learning Applications in Earth Observation: A Systematic Review](https://mlsanity.com/paper/2506.03938/) (2025)
- [Intriguing Property and Counterfactual Explanation of GAN for Remote Sensing Image Generation](https://mlsanity.com/paper/2303.05240/) (2023)
- [MT-HCCAR: Multi-Task Deep Learning with Hierarchical Classification and Attention-based Regression for Cloud Property Retrieval](https://mlsanity.com/paper/2401.16520/) (2024)
- [MLMT-CNN for Object Detection and Segmentation in Multi-layer and Multi-spectral Images](https://mlsanity.com/paper/2407.14473/) (2024)
- [Optimizing Kernel-Target Alignment for cloud detection in multispectral satellite images](https://mlsanity.com/paper/2306.14515/) (2023)
- [Distribution-aware Interactive Attention Network and Large-scale Cloud Recognition Benchmark on FY-4A Satellite Image](https://mlsanity.com/paper/2401.03182/) (2024)
- [MCSDNet: Mesoscale Convective System Detection Network via Multi-scale Spatiotemporal Information](https://mlsanity.com/paper/2404.17186/) (2024)
- [TIRAuxCloud: A Thermal Infrared Dataset for Day and Night Cloud Detection](https://mlsanity.com/paper/2602.21905/) (2026)
- [Uncertainty-Guided Edge Learning for Deep Image Regression in Remote Sensing](https://mlsanity.com/paper/2605.05590/) (2026)
- [GeoSAM-Lite: A Lightweight Foundation Model for Onboard Remote Sensing Segmentation](https://mlsanity.com/paper/2607.03760/) (2026)

## 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.