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)
佛罗伦萨大学的研究结果更新机器学习知识g(基于机器学习的监测规划气候弹性建筑遗产保护)
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摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-人工智能的新数据在一份新的报告中呈现。根据NewsRx记者来自意大利佛罗伦萨的消息，研究表明：“极端天气事件的频率和强度的增加正在加速遗产建筑表面破坏的机制，因此寻找自动化技术来减少监测的时间和成本，并支持它们的保护是合适的。”本研究的资助者包括Spoak 7；欧盟-下一代欧盟。新闻记者引用了弗罗伦茨大学的研究：“这里提出了一种全自动化的方法，用于分割和鉴定构成Palazz o Pitti立面之一的建筑元素。这种分析的目的是提供工具，以便更详细地评估Pietraforte砂岩元素的部分剥离风险。”将机器学习技术应用于航测无人机测量获得的DEM信息的分割和分类,利用G K均值算法对分割后的目标进行无监督的几何分类,根据目标的形状识别出最脆弱的元素.
Abstract
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 ."
Key words
University of Florence/Florence/Italy/Europe/Cyborgs/Emerging Technologies/Machine Learning
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