My first paper dealing with the topic of error propagation in GIS has been accepted, and it will be presented on the ISPRS Technical Commission II Midterm Symposium in Toronto in October 2014.
To the extent of my knowledge, this is the first paper that deals with error propagation in 3D GIS/3D city modelling. The dataset that has been used is produced by my random engine Random3Dcity, which generates random CityGML buildings in multiple representations (LODs), and simulates acquisition errors.
The datasets will be released for public use soon. The data is released on the website of the engine.
The full paper (open access) can be found here:
Joint International Conference on Geospatial Theory, Processing, Modelling and Applications. Toronto, Canada. October 2014.
This paper describes the analysis of the propagation of positional uncertainty in 3D city models to the uncertainty in the computation of their volumes. Current work related to error propagation in GIS is limited to 2D data and 2D GIS operations, especially of rasters. In this research we have (1) developed two engines, one that generates random 3D buildings in CityGML in multiple LODs, and one that simulates acquisition errors to the geometry; (2) performed an error propagation analysis on volume computation based on the Monte Carlo method; and (3) worked towards establishing a framework for investigating error propagation in 3D GIS. The results of the experiments show that a comparatively small error in the geometry of a 3D city model may cause significant discrepancies in the computation of its volume. This has consequences for several applications, such as in estimation of energy demand and property taxes. The contribution of this work is twofold: this is the first error propagation analysis in 3D city modelling, and the novel approach and the engines that we have created can be used for analysing most of 3D GIS operations, supporting related research efforts in the future.