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Enhancements include converting extracted soundings into area-based maps, with initial testing on 2016 NOAA 500m-by-500m LiDAR tiles near Key West, Florida, and ongoing evaluation on 2022 tiles north of Miami Beach. Accuracy results indicate the clustering approach is instrument-neutral, adaptable, and requires minimal human intervention. The workflow couples CHRT (Cube with Hierarchical Resolution Techniques) with k-means clustering, assessed using CHRT-ML performance metrics.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/improving-shallow-water-nautical-charts-via-operational-automated-machine-learning-based-bathymetry-extraction-from-airborne-lidar-point-clouds/128766/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/improving-shallow-water-nautical-charts-via-operational-automated-machine-learning-based-bathymetry-extraction-from-airborne-lidar-point-clouds/128766.png","ImageObject",300,407,{"name":92,"@type":93},"Hazel","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-17","2026-08-06",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",14,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is the document’s main goal for shallow-water mapping?","Question",{"text":113,"@type":114},"Improve shallow-water nautical charts by automatically extracting bathymetry from airborne LiDAR point clouds using an operational automated machine learning-based approach.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How has the original method evolved from the initial proof of concept?",{"text":118,"@type":114},"The clustering-based extraction approach has been converted into software-engineered code and enhanced to produce area-based bathymetric maps.",{"name":120,"@type":111,"acceptedAnswer":121},"What performance and accuracy results are reported for the CHRT-ML workflow?",{"text":122,"@type":114},"Performance metrics indicate CHRT-ML needs about 90 minutes per tile and produces carts with a root mean squared error (RMSE) of about 5 cm.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},128766,1786003220,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":14,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":24},1099523882367,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","University of New Hampshire  \nUniversity of New Hampshire Scholars Repository  \n\n| Center for Coastal and Ocean Mapping | Center for Coastal and Ocean Mapping |\n| --- | --- |\n| 11-28-2023\u003Cbr>Improving Shallow Water Nautical Charts Via Operational Automated Machine Learning-Based Bathymetry Extraction from Airborne LiDAR Point Clouds\u003Cbr>Kim Lowell\u003Cbr>University of New Hampshire, Durham, [Kim.Lowell@unh.edu](Kim.Lowell@unh.edu)\u003Cbr>Brian Miles\u003Cbr>University of New Hampshire, Durham, [Brian.Miles@unh.edu](Brian.Miles@unh.edu)\u003Cbr>Follow this and additional works at: [https://scholars.unh.edu/ccom](https://scholars.unh.edu/ccom) |  |\n\nRecommended Citation  \nLowell, Kim and Miles, Brian, \"Improving Shallow Water Nautical Charts Via Operational Automated Machine Learning-Based Bathymetry Extraction from Airborne LiDAR Point Clouds\" (2023) . JALBTCX 2023 (Joint Airborne Lidar Bathymetry Technical Center of Expertise), Kiln, MI, November 28-30. 1461.  \n[https://scholars.unh.edu/ccom/1461](https://scholars.unh.edu/ccom/1461)  \nThis Conference Proceeding is brought to you for free and open access by the Center for Coastal and Ocean Mapping at University of New Hampshire Scholars Repository. It has been accepted for inclusion in Center for Coastal and Ocean Mapping by an authorized administrator of University of New Hampshire Scholars Repository. For more information, [please contact](please contact Scholarly.Communication@unh.edu)[ Scholarly.Communication@unh.edu](please contact Scholarly.Communication@unh.edu).  \nImproving Shallow Water Nautical Charts Via Operational Automated Machine Learning-based Bathymetry Extraction from Airborne LiDAR Point Clouds  \nKim Lowell, Research Scientist, Brian Miles, Senior Research Project Engineer  \nCenter for Coastal and Ocean Mapping, University of New Hampshire [klowell@ccom.unh.edu](klowell@ccom.unh.edu)  \n[A proof-of-concept clustering-based approach to automatically extracting shallow water bathymetric](A proof-of-concept clustering-based approach to automatically extracting shallow water bathymetric)[ ](A proof-of-concept clustering-based approach to automatically extracting shallow water bathymetric)[soundings from airborne LiDAR point clouds has been converted to software-engineered code and](soundings from airborne LiDAR point clouds has been converted to software-engineered code and)[ ](soundings from airborne LiDAR point clouds has been converted to software-engineered code and)[continues to evolve. This has brought enhancements including the conversion of extracted bathymetric](continues to evolve. This has brought enhancements including the conversion of extracted bathymetric)[ ](continues to evolve. This has brought enhancements including the conversion of extracted bathymetric)[soundings to area-based maps. The original testbed was 2016 500m-by-500m NOAA LiDAR tiles near Key](soundings to area-based maps. The original testbed was 2016 500m-by-500m NOAA LiDAR tiles near Key)[ ](soundings to area-based maps. The original testbed was 2016 500m-by-500m NOAA LiDAR tiles near Key)[West](West), Florida; currently being evaluated are 2022 tiles north of Miami Beach. Accuracy evaluation for the two data sets suggest the clustering approach is instrument-neutral, readily adaptable, and requires minimal human intervention. The analytical approach that couples a widely used approach used for sonar data (CHRT – “Cube with Hierarchical Resolution Techniques”) with k-means clustering will be described as will the operational workflow. Performance metrics suggest that “CHRT-ML” (CHRT with Machine Learning) requires about 90 minutes of processing time per tile, and the carts that result have a root mean squared error (RMSE) of about 5 cm.","cbCaiaiY3vuLUSHG","https://ap.wps.com/l/cbCaiaiY3vuLUSHG","pdf",90029,"English","# Background and Motivation\n## From Proof of Concept to Operational Workflow\n# Data Sets and Testbed Locations\n## 2016 NOAA Tiles near Key West\n## 2022 Tiles north of Miami Beach\n# Methodology and Approach\n## CHRT with k-means Clustering\n## CHRT-ML Operational Workflow\n# Performance and Accuracy Evaluation\n## Processing Time\n## RMSE Results","[{\"question\":\"What is the document’s main goal for shallow-water mapping?\",\"answer\":\"Improve shallow-water nautical charts by automatically extracting bathymetry from airborne LiDAR point clouds using an operational automated machine learning-based approach.\"},{\"question\":\"How has the original method evolved from the initial proof of concept?\",\"answer\":\"The clustering-based extraction approach has been converted into software-engineered code and enhanced to produce area-based bathymetric maps.\"},{\"question\":\"What performance and accuracy results are reported for the CHRT-ML workflow?\",\"answer\":\"Performance metrics indicate CHRT-ML needs about 90 minutes per tile and produces carts with a root mean squared error (RMSE) of about 5 cm.\"}]","Improving Shallow Water Nautical Charts Via Operational Automated Machine Learning-Based Bathymetry Extraction from Airborne LiDAR Point Clouds | PDF"]