Measures the spatial quality of geographical data, horizontally and vertically, by comparison with a more accurate geospatial technique, where the statistical root mean square error (horizontal and vertical) is calculated based on the mean of the squared positional errors at checkpoints.
Published in Chapter:
Future 3D Urbanism: From UAV Data Modelling to Information Visualization
Rossana Estanqueiro (Interdisciplinary Centre of Social Sciences (CICS.NOVA), NOVA FCSH, Universidade NOVA de Lisboa, Portugal), José António Tenedório (Interdisciplinary Centre of Social Sciences (CICS.NOVA), NOVA FCSH, Universidade NOVA de Lisboa, Portugal), Carla Rebelo (Interdisciplinary Centre of Social Sciences (CICS.NOVA), NOVA FCSH, Universidade NOVA de Lisboa, Portugal), and Joao Pedro Marques (GEODRONE, Portugal & Interdisciplinary Centre of Social Sciences (CICS.NOVA), NOVA FCSH, Universidade NOVA de Lisboa, Portugal)
Copyright: © 2021
|Pages: 34
DOI: 10.4018/978-1-7998-2249-3.ch018
Abstract
Urbanism has mainly used 2D data for both the urban analysis and diagnosis and the presentation of proposals for changes in the whole city or parts of the city. Even regarding the production of urban indicators, using, for example, the quantification of the existing green area in relation to the resident population, this practice is regularly based on the area and rarely on volume. This situation is mainly justified by the sluggishness and costs associated with obtaining 3D data. The recent development of data collection technology by unmanned aerial vehicles has triggered a change in this scenario. This chapter presents the UAV data acquisition and processing chain, analyses the positional accuracy of UAV data processing performed with GCP measurements obtained from GNSS, demonstrates how positional accuracy assessment and UAV workflow's quality control are relevant for ensuring the accuracy of derived UAV geospatial products, and demonstrates the usability of 3D models in a theoretical 3D urbanism context.