AUTOMATED PROCESSING OF POINT CLOUDS TO UPDATE LAND REGISTRY MAPS
Keywords: Land registry map, geometric quality, point cloud techniques, classification, segmentation, surface normal, region growing, RANSAC, Normalised Digital Surface Model, edge detection
Abstract. The quality control, maintenance, and renewal of land registry maps have always been priorities in the surveying profession. Many countries worldwide must face the issue that a significant part of their current digital land registry maps are based on old analogue maps that were digitised without involving any in-situ measurements. A direct consequence of this is that the digitised maps' accuracy leaves much to be desired and lags behind maps based on either correct survey or numerical data. Moreover, the quality of existing digital maps can be characterised by inhomogeneity that highly depends on the location. The final solution to the problem would be to carry out new surveys in the critical areas, but that has been postponed due to the lack of time and excessive costs.
However, in recent years, point cloud technologies, such as Unmanned Aerial Vehicles (UAV), Terrestrial Laser Scanners (TLS), Aerial Laser Scanners (ALS), together with Mobile Mapping Systems (MMS), have become the focus of attention in mapping. Thanks to these technologies, experts can survey large areas with the necessary and homogenous accuracy, high resolution, and significantly, very rapidly. It is beyond doubt that these modern technologies benefit the process of updating old and less relevant maps.
Another underlying aspect worth considering is the automation in data processing since a massive amount of data needs to be evaluated. Some algorithms and their validation on study areas in Hungary are presented in this paper. Our study focuses on the mapping of buildings using point clouds generated from UAV images.