Cloud Identification from All-sky Camera Data with Machine Learning

Most ground-based observatories are equipped with wide-angle all-sky cameras to monitor the night sky conditions. Such camera systems can be used to provide an early warning of incoming clouds that can pose a danger to the telescope equipment through precipitation, as well as for sky quality monitoring. We investigate the use of different machine-learning approaches for automating the identification of mostly opaque clouds in all-sky camera data as a cloud warning system. In a deep-learning approach, we train a residual neural network (ResNet) on pre-labeled camera images. Our second approach extracts relevant and localized image features from camera images and uses these data to train a gradient-boosted tree-based model (lightGBM). We train both model approaches on a set of roughly 2000 images taken by the all-sky camera located at Lowell Observatory's Discovery Channel Telescope, in which the presence of clouds has been labeled manually. The ResNet approach reaches an accuracy of 85% in detecting clouds in a given region of an image, but requires a significant amount of computing resources. Our lightGBM approach achieves an accuracy of 95% with a training sample of ∼1000 images and rather modest computing resources. Based on different performance metrics, we recommend the latter feature-based approach for automated cloud detection. Code that was built for this work is available online.