Machine Learning-Based Monitoring for Planning Climate-Resilient Conservation of Built Heritage

Abstract

The increasing frequency and intensity of extreme weather events are accelerating the mechanisms of surface degradation of heritage buildings, and it is therefore appropriate to find automatic techniques to reduce the time and cost of monitoring and to support their planned conservation. A fully automated approach is presented here for the segmentation and classification of the architectural elements that make up one of the façades of Palazzo Pitti. The aim of this analysis is to provide tools for a more detailed assessment of the risk of detachment of parts of the pietraforte sandstone elements. Machine learning techniques were applied for the segmentation and classification of information from a DEM obtained via a photogrammetric drone survey. An unsupervised geometry-based classification of the segmented objects was performed using K-means for identifying the most vulnerable elements according to their shapes. The results were validated through comparing them with those obtained via manual segmentation and classification, as well as with studies carried out by experts in the field. The initial results, which can be integrated with non-geometric information, show the usefulness of drone surveys in the context of automatic monitoring of heritage buildings.