# CloudSegNet: A Deep Network for Nychthemeron Cloud Image Segmentation

arXiv: https://arxiv.org/abs/1904.07979 · HTML: https://mlsanity.com/paper/1904.07979/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2019.

## Built on by (most cited first, 15 of 15)

- [Using GANs to Augment Data for Cloud Image Segmentation Task](https://mlsanity.com/paper/2106.03064/) (2021)
- [DDUNet: Dual Dynamic U-Net for Highly-Efficient Cloud Segmentation](https://mlsanity.com/paper/2501.15385/) (2025)
- [Water Simulation and Rendering from a Still Photograph](https://mlsanity.com/paper/2210.02553/) (2022)
- [UCloudNet: A Residual U-Net with Deep Supervision for Cloud Image Segmentation](https://mlsanity.com/paper/2501.06440/) (2025)
- [Using U-Net Network for Efficient Brain Tumor Segmentation in MRI Images](https://mlsanity.com/paper/2211.01885/) (2022)
- [ACLNet: An Attention and Clustering-based Cloud Segmentation Network](https://mlsanity.com/paper/2207.06277/) (2022)
- [Open-Source Ground-based Sky Image Datasets for Very Short-term Solar Forecasting, Cloud Analysis and Modeling: A Comprehensive Survey](https://mlsanity.com/paper/2211.14709/) (2022)
- [LenghuSky-8: An 8-Year All-Sky Cloud Dataset with Star-Aware Masks and Alt-Az Calibration for Segmentation and Nowcasting](https://mlsanity.com/paper/2603.16429/) (2026)
- [Frequency-centroid features for word recognition of non-native English speakers](https://mlsanity.com/paper/2206.07176/) (2022)
- [A knowledge-based data-driven (KBDD) framework for all-day identification of cloud types using satellite remote sensing](https://mlsanity.com/paper/2312.00308/) (2023)
- [Multi-scale gridded Gabor attention for cirrus segmentation](https://mlsanity.com/paper/2407.08852/) (2024)
- [Segregation and Context Aggregation Network for Real-time Cloud Segmentation](https://mlsanity.com/paper/2504.14178/) (2025)
- [MPCM-Net: Multi-scale network integrates partial attention convolution with Mamba for ground-based cloud image segmentation](https://mlsanity.com/paper/2511.11681/) (2025)
- [CloudMamba: An Uncertainty-Guided Dual-Scale Mamba Network for Cloud Detection in Remote Sensing Imagery](https://mlsanity.com/paper/2604.06844/) (2026)
- [Cross-Modal Hierarchical Fusion for from Multi-Sensor Ground Observation](https://mlsanity.com/paper/2606.30647/) (2026)

## Abstract (arXiv)

We analyze clouds in the earth's atmosphere using ground-based sky cameras. An accurate segmentation of clouds in the captured sky/cloud image is difficult, owing to the fuzzy boundaries of clouds. Several techniques have been proposed that use color as the discriminatory feature for cloud detection. In the existing literature, however, analysis of daytime and nighttime images is considered separately, mainly because of differences in image characteristics and applications. In this paper, we propose a light-weight deep-learning architecture called CloudSegNet. It is the first that integrates daytime and nighttime (also known as nychthemeron) image segmentation in a single framework, and achieves state-of-the-art results on public databases.