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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Deep learning-based semantic segmentation of night-sky clouds for operational telescope scheduling.

Xuan Liu1, Hai Cao2,3, Ruojun Wang4

  • 1School of Airspace Science and Engineering, Shandong University, Weihai, 264209, Shandong, People's Republic of China.

Scientific Reports
|July 1, 2026
PubMed
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Astronomical observatories can now improve efficiency with NightCloudSegNet, a new deep learning model for real-time cloud detection. This system provides crucial dome-scale cloud coverage data, optimizing telescope scheduling and preventing data loss.

Area of Science:

  • Astronomy
  • Computer Vision
  • Machine Learning

Background:

  • Ground-based optical telescopes require precise, real-time cloud data for efficient operation.
  • Inaccurate or delayed atmospheric information leads to wasted observation time and lost scientific data.

Purpose of the Study:

  • To introduce the WOANC dataset and NightCloudSegNet, a novel framework for cloud segmentation in astronomical imaging.
  • To enhance automated telescope scheduling and shuttering decisions through accurate cloud coverage assessment.

Main Methods:

  • Developed NightCloudSegNet, a fisheye-aware segmentation framework tailored for low-light conditions.
  • Utilized the WOANC dataset, a pixel-annotated nighttime full-dome dataset from an operational observatory.
  • Evaluated model performance on both the WOANC and external SWINSEG datasets.
Keywords:
All-sky fisheye imagingAstronomical automationDeep-learning semantic segmentationNighttime cloud detectionObservability assessmentTelescope scheduling

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Main Results:

  • NightCloudSegNet achieved 86.6% mIoU and 92.8% F1 score on the WOANC test set.
  • The model demonstrated robust performance on the SWINSEG dataset with 86.2% mIoU and 92.6% F1 score.
  • Successfully translated segmentation masks into per-target observability indicators.

Conclusions:

  • NightCloudSegNet offers a reliable solution for real-time cloud detection in astronomical observatories.
  • The framework's performance indicates its potential to significantly improve observational efficiency in automated telescope operations.
  • Accurate cloud segmentation supports informed decisions, maximizing the use of valuable telescope resources.