Abstract
LIDAR (LIght Detection And Ranging) data are a primary data source for digital terrain model (DTM) generation and 3D city models. This paper presents a three-stage framework for a robust automatic classification of raw LIDAR data as buildings, ground and vegetation, followed by a reconstruction of 3D models of the buildings. In the first stage the raw data are filtered and interpolated over a grid. In the second stage, first a double raw data segmentation is performed and then geometric and topological relationships among regions resulting from segmentation are computed and stored in a knowledge base. In the third stage, a rule-based scheme is applied for the classification of the regions. Finally, polyhedral building models are reconstructed by analysing the topology of building outlines, building roof slopes and eaves lines. Results obtained on data sets with different ground point density, gathered over the town of Pavia (Italy) with Toposys and Optech airborne laser scanning systems, are shown to illustrate the effectiveness of the proposed approach.
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This work has been partly financed under the Italian national research project COFIN no. 9808229941_002.
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Forlani, G., Nardinocchi, C., Scaioni, M. et al. Complete classification of raw LIDAR data and 3D reconstruction of buildings. Pattern Anal Applic 8, 357–374 (2006). https://doi.org/10.1007/s10044-005-0018-2
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DOI: https://doi.org/10.1007/s10044-005-0018-2