UCloudNet: A Residual U-Net with Deep Supervision for Cloud Image Segmentation

Yijie Li, Hewei Wang, Shaofan Wang, Yee Hui Lee, Muhammad Salman Pathan +1 more
1/11/2025
cs.CVeess.IV

No official code found. Best implementation: Att100/UCloudNet (7 stars, last commit 2024-12-23).

Abstract

Recent advancements in meteorology involve the use of ground-based sky cameras for cloud observation. Analyzing images from these cameras helps in calculating cloud coverage and understanding atmospheric phenomena. Traditionally, cloud image segmentation relied on conventional computer vision techniques. However, with the advent of deep learning, convolutional neural networks (CNNs) are increasingly applied for this purpose. Despite their effectiveness, CNNs often require many epochs to converge, posing challenges for real-time processing in sky camera systems. In this paper, we introduce a residual U-Net with deep supervision for cloud segmentation which provides better accuracy than previous approaches, and with less training consumption. By utilizing residual connection in encoders of UCloudNet, the feature extraction ability is further improved.

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Code Implementations(1)

Att100/UCloudNetCommunity100%
71Python, Jupyter NotebookFeb 15, 20221 years ago

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Cite this paper

@article{li2025ucloudnet,
  title  = {UCloudNet: A Residual U-Net with Deep Supervision for Cloud Image Segmentation},
  author = {Yijie Li and Hewei Wang and Shaofan Wang and Yee Hui Lee and Muhammad Salman Pathan and Soumyabrata Dev},
  year   = {2025},
  eprint = {2501.06440},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2501.06440v1}
}

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