[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121925-en":3,"doc-seo-121925-105":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":20,"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},121925,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","A machine learning approach to the geomorphometric detection of ribbed moraines in Norway","Machine learning is leveraged to address gaps in geomorphological mapping, where manual and semi-automated workflows are constrained by data resolution, coverage, and human subjectivity. The study develops an unsupervised machine-learning method that extracts morphometrics from high-resolution digital elevation models and masks unlikely formation regions to detect ribbed moraines. Multiple algorithms are tested, with K-means clustering identified as the best performer across three 15 km² study areas in Norway.","Earth Surf. Dynam., 12, 801–818, 2024  \n[https://doi.org/10.5194/esurf-12-801-2024](https://doi.org/10.5194/esurf-12-801-2024)[ ](https://doi.org/10.5194/esurf-12-801-2024)© Author(s) 2024 . This work is distributed under the Creative Commons Attribution 4 .0 License.  \nA machine learning approach to the geomorphometric detection of ribbed moraines in Norway  \nThomas J. Barnes, Thomas V. Schuler, Simon Filhol, and Karianne S. Lilleøren  \nDepartment of Geosciences, University of Oslo, 0316 Oslo, Norway  \nCorrespondence: Thomas J. Barnes ([thomas.barnes@geo.uio.no](thomas.barnes@geo.uio.no))  \nReceived: 19 October 2023 – Discussion started: 6 November 2023  \nRevised: 12 April 2024 – Accepted: 29 April 2024 – Published: 10 June 2024  \nAbstract. Machine learning is a powerful yet underutilised tool in geomorphology, commonly used for imagebased pattern recognition. Analysing new high-resolution (1–10 m) elevation datasets, we investigate its usefulness for detecting discrete geomorphological features. This study develops a machine-learning-based method for identifying ribbed moraines in digital elevation data and progresses to test its performance versus timeconsuming, manual methods. Ribbed moraines share geomorphometric characteristics with other glacial landforms, hence representing a valuable test of our new methodology in terms of differentiating between similar features, and for detecting landforms with similar characteristics. Furthermore, mapping ribbed moraines may provide valuable indications of their origin, a topic of debate within glacial geomorphology. To automatically detect ribbed moraines, we extract simple morphometrics from high-resolution digital elevation model data and mask regions where ribbed moraines are unlikely to form. We then test several machine learning algorithms before examining the best performer (K-means clustering) for three study areas of 15 km2 in Norway. Our results demonstrate a balanced accuracy of 65 %–75 % when validating versus ground-truthing. The performance depends on the availability of high-resolution elevation data in Norway that are needed to resolve the spatial scale of the target (10–100 m) . We find the method effective at detecting both fields of ribbed moraines, as well as individual ribbed moraines. We propose pathways for the future implementation of this method on a large scale and for increasing the detail of information gained about detected landforms. In conclusion, we demonstrate K-means clustering as a promising method for detecting ribbed moraines, with great potential to reduce the time needed to produce landform maps.  \n1 Introduction  \n1.1 Geomorphological mapping  \nMapping of landforms has traditionally been a manual process, either through direct field observations or manual analysis of remotely sensed data (Smith and Clark, 2005; Verstappen, 2011; Evans, 2012; Sommerkorn, 2020) . More recently, semi-automated methods have been developed, where computational analysis of remote sensing data is interpreted by the operator (Guitet et al., 2013), giving rise to subjectivity and human error (Saha et al., 2011; Eisank et al., 2014; Sommerkorn, 2020) . Often, the restriction of data availability, quality, and resolution have been the primary limitations leading to the maintenance of traditional approaches, as the  \nresolution of digital elevation models (DEMs) has typically been limited to 30–300 m (Saha et al., 2011; Iwahashi et al., 2018), thus inhibiting the detection of metre-scale features. Additionally, until recently, sub-10 m datasets have been afflicted with patchy coverage (UKEA, 2023), limiting largescale, high-resolution digital mapping of smaller (sub-dataresolution) landforms.  \n1.2 Machine learning as a solution  \nIn recent years, automated landform mapping has taken a machine-learning-based pattern recognition approach, starting with a DEM and extracting morphometrics from input data, such as slope, aspect, convexity, and surface texture (Clubb et al., 2019","cbCaivNIsp3EBEYt","https://ap.wps.com/l/cbCaivNIsp3EBEYt","pdf",9027974,1,18,"English","en",105,"# Introduction\n## Geomorphological mapping\n## Machine learning as a solution\n## Study aims","[{\"question\":\"Why is machine learning used for geomorphological mapping in this work?\",\"answer\":\"Because it reduces labor intensity and minimizes human error compared with manual or semi-automated workflows, and can exploit high-resolution elevation data for pattern recognition.\"},{\"question\":\"How are ribbed moraines detected from elevation data?\",\"answer\":\"Morphometrics are extracted from high-resolution DEMs, regions where ribbed moraines are unlikely to form are masked, and machine learning algorithms are applied to identify the features.\"},{\"question\":\"Which machine learning algorithm performs best and how is it evaluated?\",\"answer\":\"K-means clustering is examined as the best performer, and results are validated against ground-truthing with a reported balanced accuracy of about 65%–75%.\"}]","A machine learning approach to the geomorphometric detection of ribbed moraines in Norway | 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is machine learning used for geomorphological mapping in this work?","Question",{"text":75,"@type":76},"Because it reduces labor intensity and minimizes human error compared with manual or semi-automated workflows, and can exploit high-resolution elevation data for pattern recognition.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are ribbed moraines detected from elevation data?",{"text":80,"@type":76},"Morphometrics are extracted from high-resolution DEMs, regions where ribbed moraines are unlikely to form are masked, and machine learning algorithms are applied to identify the features.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning algorithm performs best and how is it evaluated?",{"text":84,"@type":76},"K-means clustering is examined as the best performer, and results are validated against ground-truthing with a reported balanced accuracy of about 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