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Automatic classification of building types in 3D city models Using SVMs for semantic enrichment of low resolution building data

机译：在3D城市模型中使用SVM对低分辨率建筑数据进行语义丰富的自动分类

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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.
机译：本文提出了一种基于支持向量机（SVM）的分类器，这是一种通过推导建筑物类型来对3D粗略城市模型进行语义丰富的高级机器学习方法。建筑物类型（独立建筑物，梯田建筑物等）的信息对于3D城市模型的各种相关应用（如空间营销，房地产管理和营销以及可视化）至关重要。乍看之下，从粗略数据（主要是2D占地面积，建筑物高度和功能）推导建筑物类型似乎是不可能的。但是，它通过合并建筑物的空间环境而成功。由于可以很容易地以低成本获得输入数据，因此该方法可广泛应用。然而，与所有监督学习算法一样，获得标记的训练数据非常耗时。因此，我们提供了一种使用离群值检测和聚类方法来支持用户有效而迅速地获取足够训练数据的方法。

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