A multi-height LoD1 model of all buildings in the Netherlands

A multi-height LoD1 model of all buildings in the Netherlands

Dukai, B. (TU Delft Urban Data Science)
Ledoux, H. (TU Delft Urban Data Science)
Stoter, J.E. (TU Delft Urban Data Science)

2019

The 3D representation of buildings with roof shapes (also called LoD2) is popular in the 3D city modelling domain since it provides a realistic view of 3D city models. However, for many application block models of buildings are sufficient or even more suitable. These so called LoD1 models can be reconstructed relatively easily from building footprints and point clouds. But LoD1 representations for the same building can be rather different because of differences in height references used to reconstruct the block models and differences in underlying statistical calculation methods. Users are often not aware of these differences, while these differences may have an impact on the outcome of spatial analyses. To standardise possible variances of LoD1 models and let the users choose the best one for their application, we have developed a LoD1 reconstruction service that generates several heights per building (both for the ground surface and the extrusion height). The building models are generated for all ~10 million buildings in The Netherlands based on footprints of buildings and LiDAR point clouds. The 3D dataset is updated every month automatically. In addition, for each building quality parameters are calculated and made available. This article describes the development of the LoD1 building service and we report on the spatial analysis that we performed on the generated height values.

3D building models
3D building reconstruction
quality of 3D data
open data
roof classification
Quality of 3D data
Roof classification
Open data

http://resolver.tudelft.nl/uuid:039f0e8d-4a1b-4919-b3d5-2f689d06e25c

https://doi.org/10.5194/isprs-annals-IV-4-W8-51-2019

2194-9042

ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 4 (4/W8), 51-57

14th 3D GeoInfo Conference, 2019-09-24 → 2019-09-27, Singapore, Singapore

Institutional Repository

journal article

© 2019 B. Dukai, H. Ledoux, J.E. Stoter