Does a Finer Level of Detail of a 3D City Model Bring an Improvement for Estimating Shadows?

3D city models are characterised by the level of detail (LOD), which indicates their spatio-semantic complexity. Modelling data in finer LODs results in visually appealing models and opens the door for more applications, but that is at the expense of increased costs of acquisition, and larger storage footprint. In this paper we investigate whether the improvement in the LOD of a 3D building model brings more accurate shadow predictions. The result is that in most cases the improvement is negligible. Hence, the higher cost of acquiring 3D models in finer LODs is not always justified. However, the exact performance is influenced by the architecture of a building. The paper also describes challenges in experiments such as this one. For instance, defining error metrics may not always be simple, and the big picture of errors should be considered, as the impact of errors ultimately depends on the intended use case. For example, an error of a certain magnitude in estimating the shadow may not significantly affect visualisation purposes, but the same error may considerably influence the estimation of the photovoltaic potential.Urban Data Scienc

Uncertainty Challenge in Geospatial Analysis: An Approximation from the Land Use Cover Change Modelling Perspective

All data and geospatial analyses come with uncertainty. Although its importance has been widely recognized, uncertainty issues are still not correctly addressed in most of the current geospatial research. This chapter aims to provide an overview of the concepts, sources and tools to manage the uncertainty in geospatial analysis. To this end, we intend to increase the awareness about the importance of uncertainty for all geospatial data and analyses. Due to time and chapter length considerations, we address this topic from the Land Use Cover Change Modelling perspective.Agencia Estatal de InvestigaciónDepto. de GeografíaFac. de Geografía e HistoriaTRUEpu