Chisari, A.B., Guarnera, L., Ortis, A. et al. Benchmarking computer vision architectures for cloud detection from lidar ceilometer backscatter data. Vis Comput 41, 9441\u20139458 (2025). https:\/\/doi.org\/10.1007\/s00371-025-03960-3<\/em><\/p>\n\n\n\n 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 (), closely followed by ViT (), 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<\/a>.<\/p>\n<\/div>\n<\/div>\n\n\n\n <\/p>\n","protected":false},"excerpt":{"rendered":" Accurate weather monitoring depends significantly on cloud detection, a crucial process achievable through remote sensing tools such as satellite imagery and radar or through the analysis of data obtained from ceilometers. A ceilometer is a lidar-based device allowing to analyse the atmosphere and detect the presence of particles within clouds. The data retrieved from ceilometers involve analysis of the backscatter of the lidar signal returning to the surface. Given the inherent noise in this data, we leverage deep learning models to detect the presence of clouds in the data. To label the data, we take advantage of a Weather Research & Forecasting (WRF) model, which provided us with ground-truth used for validation purposes. We performed a comparative analysis with current state-of-the-art deep learning architectures on this specialist domain. This comparative analysis shows that the best model is ResNet 50, but also a transformer-based model, such as ViT, achieves great results. These preliminary results pave the scenario for future works aimed at detecting other particles composing the atmosphere, such as polluting agents that can be detected from the ceilometer backscatter data.<\/p>\n","protected":false},"author":1,"featured_media":934,"parent":930,"menu_order":290,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-933","page","type-page","status-publish","has-post-thumbnail","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.valeriogiuffrida.academy\/wp\/wp-json\/wp\/v2\/pages\/933","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.valeriogiuffrida.academy\/wp\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.valeriogiuffrida.academy\/wp\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.valeriogiuffrida.academy\/wp\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.valeriogiuffrida.academy\/wp\/wp-json\/wp\/v2\/comments?post=933"}],"version-history":[{"count":1,"href":"https:\/\/www.valeriogiuffrida.academy\/wp\/wp-json\/wp\/v2\/pages\/933\/revisions"}],"predecessor-version":[{"id":936,"href":"https:\/\/www.valeriogiuffrida.academy\/wp\/wp-json\/wp\/v2\/pages\/933\/revisions\/936"}],"up":[{"embeddable":true,"href":"https:\/\/www.valeriogiuffrida.academy\/wp\/wp-json\/wp\/v2\/pages\/930"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.valeriogiuffrida.academy\/wp\/wp-json\/wp\/v2\/media\/934"}],"wp:attachment":[{"href":"https:\/\/www.valeriogiuffrida.academy\/wp\/wp-json\/wp\/v2\/media?parent=933"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}![\"\"](\"https:\/\/www.valeriogiuffrida.academy\/wp\/wp-content\/uploads\/2026\/01\/immagine_2026-01-19_175357285.png\")
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<\/a><\/figure>\n\n\ufeff@Article{Chisari2025,\nauthor={Chisari, Alessio Barbaro\nand Guarnera, Luca\nand Ortis, Alessandro\nand Patatu, Wladimiro Carlo\nand Battiato, Sebastiano\nand Giuffrida, Mario Valerio},\ntitle={Benchmarking computer vision architectures for cloud detection from lidar ceilometer backscatter data},\njournal={The Visual Computer},\nyear={2025},\nmonth={Sep},\nday={01},\nvolume={41},\nnumber={12},\npages={9441-9458},\nabstract={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{\\backslash}{\\%}{\\$}{\\$}), closely followed by ViT ({\\$}{\\$}89.36{\\backslash}{\\%}{\\$}{\\$}), 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.},\nissn={1432-2315},\ndoi={10.1007\/s00371-025-03960-3},\nurl={https:\/\/doi.org\/10.1007\/s00371-025-03960-3}\n}\n<\/pre><\/div>\n<\/div>\n<\/div>\n\n\n\nAbstract<\/h3>\n\n\n\n