[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120336-en":3,"doc-seo-120336-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120336,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Classifying the clouds of Venus using unsupervised machine learning","Venus is completely shrouded by clouds, making their morphology a key to understanding atmospheric dynamics and interactions with surface-related processes. Manual feature categorization from long-running satellite archives is labor-intensive, region-limited, and susceptible to subjective bias. The study presents an automated, objective, scalable multiscale classification framework based on unsupervised machine learning. It generates consistent-scale nadir image patches from Venus Express and Akatsuki, embeds them with a convolutional neural network, and clusters embeddings using hierarchical agglomerative clustering.","Astronomy and Computing 49 (2024) 100884  \n| Full length article\u003Cbr>Classifying the clouds of Venus using unsupervised machine learning J. Mittendorf a,b,∗, K. Molaverdikhani a,b,c, B. Ercolano a,b,c, A. Giovagnoli b,d, T. Grassi c,e\u003Cbr>a University Observatory Munich, Faculty of Physics, LMU Munich, Scheinerstr. 1, Munich, D-81679, Germany b Ludwig-Maximilians-Universität München, Geschwister-Scholl-Platz 1, Munich, D-80539, Germany c Excellence Cluster ORIGINS, Boltzmannstr. 2, Garching, D-85748, Germany\u003Cbr>d German Aerospace Center (DLR), Microwaves and Radar Institute, Münchener Straße 20, Weßling, D-82234, Germany e Max Planck Institute for Extraterrestrial Physics, Giessenbachstr. 1, Garching, D-85748, Germany |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O\u003Cbr>Dataset link: [https://github.com/jmittendo/pla](https://github.com/jmittendo/pla)[net-patch-classifier](net-patch-classifier)\u003Cbr>Keywords: Venus Venus clouds\u003Cbr>Venus atmosphere\u003Cbr>Machine learning Convolutional neural network | A B S T R A C T\u003Cbr>Because Venus is completely shrouded by clouds, they play an important role in the planet’s atmospheric dynamics. Studying the various morphological features observed on satellite imagery of the Venusian clouds is crucial to understanding not only the dynamic atmospheric processes, but also interactions between the planet’s surface structures and atmosphere. While attempts at manually categorizing and classifying these features have been made many times throughout Venus’ observational history, they have been limited in scope and prone to subjective bias. We therefore present and investigate an automated, objective, and scalable approach for their classification using unsupervised machine learning that can leverage full datasets of past, ongoing, and future missions.\u003Cbr>To achieve this, we introduce a novel framework to generate nadir observation patches of Venus’ clouds at fixed consistent scales from satellite imagery data of the Venus Express and Akatsuki missions. Such patches are then divided into classes using an unsupervised machine learning approach that consists of encoding the patch images into feature vectors via a convolutional neural network trained on the patch datasets and subsequently clustering the obtained embeddings using hierarchical agglomerative clustering.\u003Cbr>We find that our approach demonstrates considerable accuracy when tested against a curated benchmark dataset of Earth cloud categories, is able to identify meaningful classes for global-scale (3000 km) cloud features on Venus and can detect small-scale (25 km) wave patterns. However, at medium scales (∼500 km) challenges are encountered, as available resolution and distinctive features start to diminish and blended features complicate the separation of well defined clusters. |  |\n\n1. Introduction  \nVenus is enveloped by a thick layer of clouds that are nearly featureless in visible light but show many variable features in the UV spectrum (e.g. Rossow et al., 1980), particularly around 365 nm – the characteristic wavelength of the unknown UV absorber (e.g. Molaverdikhani et al., 2012). Studying the morphology and temporal evolution of these features can give insights into the dynamic atmospheric processes, as well as interactions between the planet’s atmosphere and surface structures such as gravity waves, which may manifest themselves as visible wave trains at the cloud tops (Piccialli et al., 2014). This endeavor is crucial for understanding the energy transfer between different atmospheric layers and the global climate system of Venus.  \nOver the past decades, significant efforts at categorizing, classifying, and explaining the different types of cloud features found on Venus by carefully examining satellite images from missions such as the Pioneer  \nVenus Orbiter (e.g. Rossow et al., 1980), Venus Express (e.g. Titov et al., 2012), and Akatsuki (e.g. Limaye et al., 2018; Peralta et al., 2019) have been made. However, manual classification","cbCailrXRHEzqZaU","https://ap.wps.com/l/cbCailrXRHEzqZaU","pdf",7052326,1,13,"English","en",105,"# Introduction\n## Motivation and background\n## Limitations of manual classification\n## Automated unsupervised multiscale approach","[{\"question\":\"Why are Venus cloud features important to study?\",\"answer\":\"Because the clouds strongly shape Venus’s atmospheric dynamics, and their morphology helps reveal dynamic processes and possible interactions with atmospheric and surface-related phenomena.\"},{\"question\":\"What automated method does the paper propose?\",\"answer\":\"It generates consistent-scale nadir patches from Venus Express and Akatsuki images, encodes patch images into feature vectors with a convolutional neural network, and clusters the embeddings using hierarchical agglomerative clustering.\"},{\"question\":\"How does the approach perform across different spatial scales?\",\"answer\":\"It shows considerable accuracy on a benchmark dataset of Earth cloud categories, identifies meaningful global-scale (3000 km) Venus cloud classes, and detects small-scale (25 km) wave patterns, while medium scales (~500 km) are harder due to reduced resolution and blended features.\"}]","Classifying the clouds of Venus using unsupervised machine learning | 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are Venus cloud features important to study?","Question",{"text":75,"@type":76},"Because the clouds strongly shape Venus’s atmospheric dynamics, and their morphology helps reveal dynamic processes and possible interactions with atmospheric and surface-related phenomena.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What automated method does the paper propose?",{"text":80,"@type":76},"It generates consistent-scale nadir patches from Venus Express and Akatsuki images, encodes patch images into feature vectors with a convolutional neural network, and clusters the embeddings using hierarchical agglomerative clustering.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the approach perform across different spatial scales?",{"text":84,"@type":76},"It shows considerable accuracy on a benchmark dataset of Earth cloud categories, identifies meaningful global-scale (3000 km) Venus cloud classes, and detects small-scale (25 km) wave patterns, while medium scales (~500 km) are harder 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