Point Cloud Segmentation and Semantic Annotation AIded by Gis Data for Heritage Complexes

Abstract. Point cloud segmentation is an important first step in categorising a raw point cloud data. This step is necessary in order to better manage the data and generate other derivative products, e.g. 3D GIS or HBIM. The idea presented in this paper involves the use of 2D GIS to help in the segmentation, classification, as well as (early) semantic annotation of the point cloud. This derives from the fact that in the case of heritage complex sites, often times the site has been previously documented in a 2D GIS often with attributes and entities. We used this 2D data to help in the segmentation of a 3D point cloud, with the added benefit of automatic extraction and annotation of the related semantic information directly to the segmented clusters. Results show that the developed algorithm performs well with TLS data of spread out heritage sites, with a median success rate of 93% and an average rate of 86%. While manual intervention is still inevitable in some parts of the workflow (e.g. creation of the base shapefiles and choice of object segmentation order), the developed algorithm has shown to significantly reduce overall processing time and resources required in terms of segmentation and semantic annotation of a point cloud in the case of heritage complexes.