3D ISOVIST PROCESSING METHOD USING DEEP LEARNING FOR VISIBILITY EVALUATION

被引：0

作者：

Fukumoto K. ^{[1
]}

Toba J. ^{[1
]}

Horie S. ^{[1
]}

Maeda Y. ^{[1
]}

Kado K. ^{[1
]}

机构：

[1] Graduate School of Science and Engineering, Chiba Univ

来源：

AIJ Journal of Technology and Design | 2023年 / 29卷 / 73期

关键词：

Deep learning; Deep metric learning; Isovist; Point cloud; Unsupervised clustering;

D O I：

10.3130/aijt.29.1642

中图分类号：

学科分类号：

摘要：

In the field of architecture and urban planning, the isovist theory is used for evaluating spaces. In this theory, metrics, such as area or edge length, are employed to simplify higher-dimension isovist volumes. In this study, we propose a visibility evaluation method using a deep neural network as a feature extractor that extracts features from isovist point clouds. A classification and clustering network were tested by evaluating five architectures. The results show that the networks can extract valuable features and analyze the visibility using architectural characters, spatial spread, their direction, etc. © 2023 Architectural Institute of Japan. All rights reserved.

引用

页码：1642 / 1647

页数：5

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