Abstract
AbstractThis article proposes a novel method for the 3D reconstruction of LoD2 buildings from LiDAR data. We propose an active sampling strategy which applies a cascade of filters focusing on promising samples at an early stage, thus avoiding the pitfalls of RANSAC‐based approaches. Filters are based on prior knowledge represented by (nonparametric) density distributions. In our approach samples are pairs of surflets—3D points together with normal vectors derived from a plane approximation of their neighborhood. Surflet pairs provide parameters for model candidates such as azimuth, inclination and ridge height, as well as parameters estimating internal precision and consistency. This provides a ranking of roof model candidates and leads to a small number of promising hypotheses. Building footprints are derived in a preprocessing step using machine learning methods, in particular support vector machines.
Highlights
Three-dimensional (3D) city models at level of detail 2 (LoD2) according to the CityGML specification (Gröger, Kolbe, Nagel, & Häfele, 2012)—buildings with prototypical roofs and larger roof structures such as dormers or turrets—are a requirement for many applications such as solar radiation calculation (Alam, Coors, Zlatanova, & Van Oosterom, 2011), real-time simulations for training (Randt, Bildstein, & Kolbe, 2007), or visualization (Döllner, Baumann, & Buchholz, 2006). Such data are inevitable for applications which require the calculation of the Transactions in GIS. 2020;00:1–22.
The contribution of this article is a new method for deriving LoD2 buildings with larger roof structures from LiDAR data, which overcomes these and similar problems arising in other approaches
The contribution of this article is a new method for the automatic derivation of LoD2 building models with larger roof structures from LiDAR data
Summary
Three-dimensional (3D) city models at level of detail 2 (LoD2) according to the CityGML specification (Gröger, Kolbe, Nagel, & Häfele, 2012)—buildings with prototypical roofs and larger roof structures such as dormers or turrets—are a requirement for many applications such as solar radiation calculation (Alam, Coors, Zlatanova, & Van Oosterom, 2011), real-time simulations for training (Randt, Bildstein, & Kolbe, 2007), or visualization (Döllner, Baumann, & Buchholz, 2006). The contribution of this article is a new method for deriving LoD2 buildings with larger roof structures from LiDAR data, which overcomes these and similar problems arising in other approaches. The approach of Kada and McKinley (2009) uses normal vectors for the determination of the models which best fit the point cloud.
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