Abstract

Nighttime ground-based astronomical observations are often hindered by unpredictable cloud cover, which significantly reduces observing efficiency and complicates manual schedule adjustments. We present a deep learning-based method that automatically reorders observation sequences to avoid cloud-obscured sky regions. A semantic segmentation dataset of all-sky fisheye images is constructed, and a DeepLabV3-MobileNetV3 model is trained to classify “observable” versus “unobservable” areas in real time. Through astrometric calibration, each pixel is precisely mapped to altitude–azimuth coordinates, enabling the system to check whether a scheduled target falls into an observable region. When a target is predicted to be obstructed, a rule-based replanning module dynamically selects a suitable alternative from the remaining targets, respecting altitude and Moon-separation constraints. The method is validated on real observation sequences from the Nearby Galaxy Supernova Survey at Xinglong Observatory. Replanned sequences achieve observable rates above 90% under partially cloudy conditions, compared to original rates often below 10%. This work demonstrates that integrating all-sky camera semantic segmentation with astrometric calibration and intelligent rescheduling can robustly mitigate cloud-induced downtime, paving the way toward fully autonomous observatory operations and embodied intelligent telescopes.

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