Abstract
Abstract. The paper reviews the state-of-the-art in 3D city models and building block generation, with a description of the most common solutions and approaches. Then the digital reconstruction and comparison of LoD1 and LoD2 building models obtained with commercial packages and using different input data are presented. As input data, a DSM at 1m resolution derived from a GeoEye-1 stereo-pair, a DSM from an aerial block at 50 cm GSD and a LiDAR-based DSM at 1 m resolution are used. The geometric buildings produced with each dataset are evaluated with respect to some ground-truth measurements but also compared between them. Problems such as the quality of the input DSM, the accuracy of the necessary vector datasets containing the building footprints, the flexibility of the approaches and the potentialities of each dataset will be discussed. As reconstruction of the building models is largely influenced by the quality of the building footprints, which may be out-of-date or slightly shifted with respect to the employed DSMs/DTMs, an in-house method is being developed to derive them starting from the produced DSMs.
Highlights
The way of representing Earth has changed in the last years
Different typologies of building models can be classified according to their level of detail (LoD) (Kolbe et al, 2005; Haala and Brenner, 1999)
The LoD in 3D building reconstruction depends on the quality of the input data, in terms of density and accuracy
Summary
The way of representing Earth has changed in the last years. Classical two-dimensional (2D) maps have turned into threedimensional (3D) realistic representations of rural and urban environment. This paper deals with the digital reconstruction of LoD1 and LoD2 building models obtained with commercial packages and different input data in order to assess the achieved geometric accuracy of the 3D building models. Data acquired at conventional (e.g. 1 m resolution) resolutions and provided by public administrations are used to assess the geometric accuracy in the building extraction and to associate the model quality to the input data. For this purpose, combinations of 2D and 3D data have been considered.
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