Ground-based time-domain observatories require minute-by-minute, site-scale awareness of cloud cover, yet existing all-sky datasets are short, daylight-biased, or lack astrometric calibration. We present LenghuSky-8, an eight-year (2018-2025) all-sky imaging dataset from a premier astronomical site, comprising 429,620 $512 \times 512$ frames with 81.2% night-time coverage, star-aware cloud masks, background masks, and per-pixel altitude-azimuth (Alt-Az) calibration. For robust cloud segmentation across day, night, and lunar phases, we train a linear probe on DINOv3 local features and obtain 93.3% $\pm$ 1.1% overall accuracy on a balanced, manually labeled set of 1,111 images. Using stellar astrometry, we map each pixel to local alt-az coordinates and measure calibration uncertainties of approximately 0.37 deg at zenith and approximately 1.34 deg at 30 deg altitude, sufficient for integration with telescope schedulers. Beyond segmentation, we introduce a short-horizon nowcasting benchmark over per-pixel three-class logits (sky/cloud/contamination) with four baselines: persistence (copying the last frame), optical flow, ConvLSTM, and VideoGPT. ConvLSTM performs best but yields only limited gains over persistence, underscoring the difficulty of near-term cloud evolution. We release the dataset, calibrations, and an open-source toolkit for loading, evaluation, and scheduler-ready alt-az maps to boost research in segmentation, nowcasting, and autonomous observatory operations.

翻译：地基时域观测站需要分钟级、站点尺度的云层覆盖感知，然而现有的全天数据集存在时间短、偏向日间观测或缺乏天体测量校准等问题。本文介绍冷湖天空-8，这是一个来自一流天文台站、为期八年（2018-2025）的全天成像数据集，包含429,620帧$512 \times 512$图像，夜间覆盖率达81.2%，并提供了星体感知云掩码、背景掩码以及逐像素高度-方位角（Alt-Az）校准。为了在白天、夜晚及不同月相条件下实现鲁棒的云层分割，我们在DINOv3局部特征上训练了一个线性探针，在一个包含1,111张图像、经过人工标注的平衡测试集上获得了93.3% $\pm$ 1.1%的整体准确率。利用恒星天体测量学，我们将每个像素映射到本地高度-方位角坐标系，并测得校准不确定性在近天顶处约为0.37度，在30度高度角处约为1.34度，足以满足与望远镜调度系统集成的需求。除了分割任务，我们还引入了一个基于逐像素三分类逻辑值（天空/云/污染）的短时临近预报基准，并提供了四种基线方法：持续性（复制最后一帧）、光流法、ConvLSTM和VideoGPT。ConvLSTM表现最佳，但相比持续性方法仅获得有限的提升，这凸显了短期云层演变的预测难度。我们公开了该数据集、校准数据以及一个开源工具包，用于数据加载、评估和生成可直接用于调度系统的高度-方位角地图，以推动分割、临近预报及自主天文台运行领域的研究。