This paper discussed an automatic building objectivization method for generating a large number of 3D LOD2 building models by integrating 2D geospatial information, airborne LiDAR point cloud, a DEM, aerial vertical and oblique images. The procedure was divided into the following four stages. First, the elevation and ground floor elevation corresponding to each building in shapefile were determined based on LiDAR point cloud data within each building outline and a DEM, and they were appended into a building outline shapefile. Second, individual 3D LOD1 building models with the standard file format of object file presented by triangulation networks were transformed from every single building outline shapefile into the object file, and even 3D LOD1 building models with any atriums could be produced. Third, from aerial vertical and oblique images as well as whole 3D LOD1 building models, the texture corresponding to each 3D LOD1 building model triangulation was determined by the most appropriate images for texture mapping. Fourth, each complete 3D LOD2 building objectivization model was constructed by splitting and packing its corresponding building texture images into a single image for data compression. Finally, the objectivization models were imported into the WebGL platform for demonstrating the advanced application in this study.

Processing large number of multi-view images is complicated and tedious. Also, when matching multiple stereo pairs, it would take long in getting disparity values if each pair is to be processed independently. In addition, redundantly described scene models with low reliability trouble the exploitation of geospatial information. This paper proposes an effective matching strategy featuring in key view selection and clustering, disparity delivery and point cloud refinement to tackle the aforementioned shortcomings. The proposed approach has been tested by two practical data sets and it is proven that both the efficient image manipulation with the computational time reduction more than 80% and quality point cloud generation well depicting the scene geometry highlight the merit of this study.

To describe the systematic error of the Terrestrial Laser Scanners (TLS) more reasonably, based on the self-calibration method, a hybrid calibration theory that combines self-calibration and individual component calibration is proposed. In this study, FARO S350, Trimble SX10 and RIEGL VZ-400 were used to test at the National Land Surveying Center (NLSC). According to the results of t-test (in the 95% confidence interval), using systematic error calibration parameters can effectively simulate the TLS systemic error model. The results also show that the constant and linear calibration parameters are sufficient to describe the systematic errors of the three scanners used in this study, and the calibration parameters obtained from the individual component calibration can achieve a reduced effect. These show that the theory proposed in this study have the potential for more careful analysis of TLS calibration and evaluation of systematic errors.

The data management process of underground pipeline is divided into field data acquisition and data processing. Traditionally, the engineers at the construction site make on-site as-built drawings of the construction, and the built data is fragmented. The use of photogrammetry technology to create a three-dimensional model can record the details of the construction site more completely and independently. In-house data processing requires a lot of manpower for data input and repeated checks to avoid data errors. This paper uses python/GDAL to develop a set of digital pipeline data construction process through general photographic equipment to obtain images to build a three-dimensional model, digitizes and builds attribute data in the model, and automatically creates a correspondence according to the type of facility selected by the user. The field data can be automatically checked, including basic input rules and logical checks, to assist in improving the pipeline data management. Only one or two engineers are needed to complete the work so to achieve a time-saving and labor-saving digital underground pipeline management process.