An adaptive building simplification approach based on shape analysis and representation

doi:10.11947/j.AGCS.2022.20210302

Abstract

Abstract: Building simplification is one of the long-standing challenges in cartography. Establishing a hybrid simplification mechanism based on shape characteristics is an effective strategy to adapt to the diversity and complexity of building shapes. However, existing studies mainly focus on local structure analysis or simplified result evaluation, lacking analytical perspective and deep understanding of the overall shapes. This study proposed a shape-adaptive building simplification approach using deep learning. First, a graph convolutional autoencoder was designed to encode the shape features implicated in the boundary of each building. Then, the mapping relationship between the shape encodings and four candidate simplification algorithms was established using a supervised learning model, so as to realize an adaptive mechanism of selecting the appropriate simplification algorithm according to the shape characteristics of the input building. Experimental results show that our approach performs better than the standalone application of existing algorithms in measuring the changes of position, orientation, area, and shape, and have good theoretical and practical significance.

Key words: building simplification, shape representation, adaptive simplification, graph convolutional autoencoder

CLC Number:

P208

YAN Xiongfeng, YUAN Tuo, YANG Min, KONG Bo, LIU Pengcheng. An adaptive building simplification approach based on shape analysis and representation[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(2): 269-278.

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