ABSTRACT

Recognition of building group patterns is of great significance for understanding and modeling the urban space. However, many current methods cannot fully utilize spatial information and have trouble efficiently dealing with topographic data with high complexity. The design of intelligent computational models that can act directly on topographic data to extract spatial features is critical. To this end, we propose a novel deep neural network based on graph convolutions to automatically identify building group patterns with arbitrary forms. The method first models buildings by a general graph, and then the neural network simultaneously learns the structural information as well as vertex attributes to classify building objects. We apply this method to real building data, and the experimental results show that the proposed method can effectively capture spatial information to make more accurate predictions than traditional methods.

摘要

认识建筑群模式对于理解和建模城市空间具有重要意义。然而，许多当前方法不能充分利用空间信息并且难以有效地处理具有高复杂度的地形数据。可以直接作用于地形数据以提取空间特征的智能计算模型的设计至关重要。为此，我们提出了一种基于图卷积的新型深度神经网络，以自动识别具有任意形式的建筑群模式。该方法首先通过通用图对建筑物进行建模，然后神经网络同时学习结构信息和顶点属性以对建筑物对象进行分类。我们将此方法应用于实际的建筑数据，