SIDEWALK DETECTION AND PAVEMENT CHARACTERISATION IN HISTORIC URBAN ENVIRONMENTS FROM POINT CLOUDS: PRELIMINARY RESULTS
Keywords: Semantic Segmentation, Classification, Mobile Laser Scanning, Cultural Heritage, Mobility, Accessibility, Urban Heritage, Pedestrian Infrastructure
Abstract. The definition of physical accessibility in urban environments is a topic of recognized importance by policy makers and by international organizations. A first step to address the accessibility topic is the definition and characterisation of urban elements, like sidewalks, roads, and ramps. Sidewalk inventory plays a crucial role in this phase. In literature there are several ways to extract sidewalks from a point cloud, but they are all tailored on modern and standardized situations. For example the presence of a curb is assumed as the normality and the roads are supposed to have the same width along the path. When dealing with an Urban Heritage, some difficulties arise. In fact, in an historic urban environment ground irregularities should be taken in consideration: the paving is composed by different materials, curbs are not always present, and a Z difference between road and sidewalks is not so sure. In such cases existing methodologies cannot be applied. This paper present a method to semantically segment a point cloud, labelling sidewalks and roads. Sidewalks are also characterized by detecting their pavings. The method is tested on an Urban Heritage: the Unesco site of Sabbioneta, in northern Italy. The results are promising, sidewalks are detected with a precision of 80%, main errors are in corner areas. Paving characterisation is based on thresholds derived from some samples, and the method shows an high precision (more than 90%) in all the pavings considered.