@article{DavidovicKuzmicVasicetal., author = {Davidovic, Marina and Kuzmic, Tatjana and Vasic, Dejan and Wich, Valentin and Brunn, Ansgar and Bulatovic, Vladimir}, title = {Methodology for Road Defect Detection and Administration based on Mobile Mapping Data}, series = {Computer Modeling in Engineering \& Sciences}, volume = {129}, journal = {Computer Modeling in Engineering \& Sciences}, number = {1}, doi = {https://doi.org/10.32604/cmes.2021.016071}, pages = {207 -- 226}, abstract = {A detailed inspection of roads requires highly detailed spatial data with sufficient precision to deliver an accurate geometry and to describe road defects visually. This paper presents a novel method for the detection of road defects. The input data for road defect detection included point clouds and orthomosaics gathered by mobile mapping technology. The defects were categorized in three major groups with the following geometric primitives: points, lines and polygons. The method suggests the detection of point objects from matched point clouds, panoramic images and ortho photos. Defects were mapped as point, line or polygon geometries, directly derived from orthomosaics and panoramic images. Besides the geometric position of road defects, all objects were assigned to a variety of attributes: defect type, surface material, center-of-gravity, area, length, corresponding image of the defect and degree of damage. A spatial dataset comprising defect values with a matching data type was created to perform the attribute analysis quickly and correctly. The final product is a spatial vector data set, consisting of points, lines and polygons, which contains attributes with further information and geometry. This paper demonstrates that mobile mapping suits a large-scale feature extraction of road infrastructure defects. By its simplicity and flexibility, the presented methodology allows it to be easily adapted to extract further feature types with their attributes. This makes the proposed approach a vital tool for data extraction settings with multiple mobile mapping data analysts, e.g., offline crowdsourcing.}, language = {en} } @inproceedings{MeyerBrunn, author = {Meyer, Theresa and Brunn, Ansgar}, title = {3D Point Clouds in PostgreSQL/PostGIS for Applications in GIS and Geodesy}, series = {Proceedings of the 5th International Conference on Geographical Information Systems Theory, Applications and Management}, booktitle = {Proceedings of the 5th International Conference on Geographical Information Systems Theory, Applications and Management}, publisher = {SCITEPRESS - Science and Technology Publications}, isbn = {978-989-758-371-1}, doi = {10.5220/0007840901540163}, pages = {154 -- 163}, abstract = {Besides the common approach of an exclusively file based management of 3D point clouds, meanwhile it is possible to store and process this special type of massive geodata within spatial database systems. Users benefit from the general advantages of database solutions and especially from the potentials of a combined analysis of original 3D point clouds, 2D rasters, 3D voxel stacks and 2D and 3D vector data in order to gain valuable geo- information. This paper describes the integration of 3D point clouds into an open source PostgreSQL/PostGIS database using the Pointcloud extension and functions of the Point Data Abstraction Library (PDAL). The focus is on performing three-dimensional spatial queries and the evaluation of different tiling methods for the organization of 3D point clouds into table rows, regarding memory space, performance of spatial queries and effects on interactions between point clouds and other GIS features within the database. A new approach for an optimized point cloud tiling, considering the individual geometric characteristic of a 3D point cloud, is presented. The results show that an individually selected storage structure for a point cloud is crucial for low memory consumption and high-performance 3D queries in PostGIS applications, taking account of its three-dimensional spatial extent and point density.}, language = {en} }