摘要
由机器人与机器学习每日新闻的新闻记者兼新闻编辑-研究人员详细介绍了人工智能的新数据。根据News Rx编辑在密歇根州霍顿的新闻报道，研究表明，"沿海悬崖因短期和长期的环境变化而侵蚀，但预测这些变化仍然是一个挑战。"我们的新闻记者从密歇根理工大学的研究中获得了一句话：“除了长期缺乏关于悬崖表面的数据外，植被的存在和生长还会使我们的侵蚀测量产生偏差，并通过掩盖悬崖表面来限制我们探测地貌侵蚀的能力。”本文建立在过去的研究基础上，将植被分为红色、绿色和绿色三个波段。Blue(RGB)图像，提出了两种在RGB图像和Structure-Fro Motion(SfM)软件构建的密集点云中分割和过滤Bar E悬崖表面植被的方法。根据先前发表的研究计算植被指数，并与机器学习(ML)点云分割模型进行了比较。
Abstract
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.”
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