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This article presents a classifier based on Support Vector Machines (SVMs), an advanced machine learning method for semantic enrichment of coarse 3D city models by deriving the building type. The information on the building type (detached building, terraced building, etc.) is essential for a variety of relevant applications of 3D city models like spatial marketing, real estate management and marketing, and for visualization. The derivation of the building type from coarse data (mainly 2D footprints, building heights and functions) seems impossible at first sight. However it succeeds by incorporating the spatial context of a building. Since the input data can be derived easily and at very low cost, this method is widely applicable. Nevertheless, as with all supervised learning algorithms, obtaining labelled training data is very time-consuming. Herewith, we provide a method which uses outlier detection and clustering methods to support users in efficiently and rapidly obtaining adequate training data.

This 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.

<p><strong>Abstract.</strong> 3D city models in Level-of-Detail 2 (LoD2) are nowadays inevitable for many applications such as solar radiation calculation and energy demand estimation. City-wide models are required which can solely be acquired by fully automatic approaches. In this paper we propose a novel method for the 3D-reconstruction of LoD2 buildings with structured roofs and dormers from LIDAR data. We apply a hybrid strategy which combines the strengths of top-down and bottom-up methods. The main contribution is the introduction of an <i>active sampling</i> strategy which applies a cascade of filters focusing on promising samples in an early stage and avoiding the pitfalls of RANSAC based approaches. Such filters are based on prior knowledge represented by (non-parametric) density distributions. Samples are pairs of surflets, i.e. 3D points together with normal vectors derived from a plane approximation of their neighborhood. Surflet pairs imply immediately important roof parameters such as azimuth, inclination and ridge height, as well as parameters for internal precision and consistency, giving a good base for assessment and ranking. Ranking of samples leads to a small number of promising hypotheses. Model selection is based on predictions for example of ridge positions which can easily be falsified based on the given observations. Our approach does not require building footprints as prerequisite. They are derived in a preprocessing step using machine learning methods, in particular Support Vector Machines (SVM).</p>