首页|Research Results from University of Florence Update Knowledge of Machine Learnin g (Machine Learning-Based Monitoring for Planning Climate-Resilient Conservation of Built Heritage)
Research Results from University of Florence Update Knowledge of Machine Learnin g (Machine Learning-Based Monitoring for Planning Climate-Resilient Conservation of Built Heritage)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on artificial intelligence are presented in a new report. According to news originating from Florence, Ital y, by NewsRx correspondents, research stated, "The increasing frequency and inte nsity of extreme weather events are accelerating the mechanisms of surface degra dation of heritage buildings, and it is therefore appropriate to find automatic techniques to reduce the time and cost of monitoring and to support their planne d conservation." Funders for this research include Spoke 7; European Union-next Generation Eu. The news reporters obtained a quote from the research from University of Florenc e: "A fully automated approach is presented here for the segmentation and classi fication of the architectural elements that make up one of the facades of Palazz o Pitti. The aim of this analysis is to provide tools for a more detailed assess ment of the risk of detachment of parts of the pietraforte sandstone elements. M achine learning techniques were applied for the segmentation and classification of information from a DEM obtained via a photogrammetric drone survey. An unsupe rvised geometry-based classification of the segmented objects was performed usin g K-means for identifying the most vulnerable elements according to their shapes ."
University of FlorenceFlorenceItalyEuropeCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
