Benchmarking computer vision architectures for cloud detection from lidar ceilometer backscatter data

Cloud detection is fundamental for accurate weather monitoring, often achieved through remote sensing technology, such as satellite imagery or radar. This study explores the use of lidar ceilometer backscatter data, a rich but noisy source of atmospheric information, to enhance cloud detection. Leveraging data acquired from a Lufft CHM 15k ceilometer over three months near Mount Etna, Italy, we gathered a novel dataset comprising time-height plots derived from backscatter profiles. The Weather Research and Forecasting (WRF) model was used for ground-truth data labeling, ensuring reliable model validation. We benchmarked state-of-the-art deep learning architectures, including CNN-based models (e.g., ResNet50, VGG16, InceptionV3, EfficientNet) and the Vision Transformer (ViT), on our collected dataset. Among these, ResNet50 achieved the highest accuracy (89.57%), closely followed by ViT (89.36%), showcasing the efficacy of residual learning and transformer-based approaches in extracting complex patterns from atmospheric data. Our results highlight the potential of lidar-based systems for accurate cloud detection, complementing other remote sensing technologies. Our work contributes to the field by introducing a publicly available dataset and providing comprehensive benchmarking results that establish a baseline for future research. This study also opens avenues for broader applications of ceilometer data, such as the detection of pollutants and other atmospheric phenomena. Our dataset is publicly available at https://zenodo.org/records/10616434.