Spatial ETL for 3D Building Modelling Based on Unmanned Aerial Vehicle Data in Semi-Urban Areas

This paper provides the innovative approach of using a spatial extract, transform, load (ETL) solution for 3D building modelling, based on an unmanned aerial vehicle (UAV) photogrammetric point cloud. The main objective of the paper is to present the holistic workflow for 3D building modelling, emphasising the benefits of using spatial ETL solutions for this purpose. Namely, despite the increasing demands for 3D city models and their geospatial applications, the generation of 3D city models is still challenging in the geospatial domain. Advanced geospatial technologies provide various possibilities for the mass acquisition of geospatial data that is further used for 3D city modelling, but there is a huge difference in the cost and quality of input data. While aerial photogrammetry and airborne laser scanning involve high costs, UAV photogrammetry has brought new opportunities, including for small and medium-sized companies, by providing a more flexible and low-cost source of spatial data for 3D modelling. In our data-driven approach, we use a spatial ETL solution to reconstruct a 3D building model from a dense image matching point cloud which was obtained beforehand from UAV imagery. The results are 3D building models in a semantic vector format consistent with the OGC CityGML standard, Level of Detail 2 (LOD2). The approach has been tested on selected buildings in a simple semi-urban area. We conclude that spatial ETL solutions can be efficiently used for 3D building modelling from UAV data, where the data process model developed allows the developer to easily control and manipulate each processing step.

Improving Spatial Data Quality through Spatial ETL Processes

The growing availability of spatial data related to different aspects makes managers at different levels of administration aware of the possibilities to enhance decision making processes through map visualization. Currently, the so-called location intelligence is identified as an important trend for the next-generation business intelligence solutions. However, before considering spatial data as “first-class citizens,” users that are not experts in geo-fields (e.g., cartography, surveying) should learn about possible problems that may arise while using and producing spatial data. These problems must be solved to improve spatial data quality and to increase the benefits that this data can deliver. Unfortunately, there is still a poor connection in applying scientific solutions, international standards, or technological advances for improving spatial data quality in everyday usage of this data. In this chapter, the authors refer to different problems that may exist in handling spatial data and show several examples of how these problems can be detected and solved using spatial ETL tools. Problem detection is based on a set of control parameters derived from the international standard.