首页|Reports from Michigan Technological University Advance Knowledge in Machine Lear ning (Machine Learning Vegetation Filtering of Coastal Cliff and Bluff Point Clo uds)
Reports from Michigan Technological University Advance Knowledge in Machine Lear ning (Machine Learning Vegetation Filtering of Coastal Cliff and Bluff Point Clo uds)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in artific ial intelligence. According to news reporting out of Houghton, Michigan, by News Rx editors, research stated, “Coastal cliffs erode in response to shortand lon g-term environmental changes, but predicting these changes continues to be a cha llenge.” Our news journalists obtained a quote from the research from Michigan Technologi cal University: “In addition to a chronic lack of data on the cliff face, vegeta tion presence and growth can bias our erosion measurements and limit our ability to detect geomorphic erosion by obscuring the cliff face. This paper builds on past research segmenting vegetation in three-band red, green, blue (RGB) imagery and presents two approaches to segmenting and filtering vegetation from the bar e cliff face in dense point clouds constructed from RGB images and structure-fro m-motion (SfM) software. Vegetation indices were computed from previously publis hed research and their utility in segmenting vegetation from bare cliff face was compared against machine learning (ML) models for point cloud segmentation.”
Michigan Technological UniversityHough tonMichiganUnited StatesNorth and Central AmericaCyborgsEmerging Techn ologiesMachine Learning
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