Towards Classification of Architectural Styles of Chinese Traditional Settlements Using Deep Learning: A Dataset, a New Framework, and Its Interpretability

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作者信息关键词期刊信息基础信息归属信息摘要

成果类型：

期刊论文

作者：

Han, Qing;Yin, Chao;Deng, Yunyuan;Liu, Peilin

通讯作者：

Chao Yin

作者机构：

[Liu, Peilin; Han, Qing; Deng, Yunyuan] Hengyang Normal Univ, Coll Geog & Tourism, Hengyang 421002, Peoples R China.

[Han, Qing; Deng, Yunyuan] Hengyang Normal Univ, Cooperat Innovat Ctr Digitalizat Cultural Heritag, Hengyang 421002, Peoples R China.

[Han, Qing; Deng, Yunyuan] Hengyang Normal Univ, Natl Local Joint Engn Lab Digital Preservat & Inn, Hengyang 421002, Peoples R China.

[Yin, Chao] Guangdong Acad Sci, Guangzhou Inst Geog,Guangdong Open Lab Geospatial, Guangdong Prov Engn Lab Geog Spatiotemporal Big D, Key Lab Guangdong Utilizat Remote Sensing & Geog, Guangzhou 510070, Peoples R China.

[Yin, Chao] Hong Kong Univ Sci & Technol, Dept Civil & Environm Engn, Hong Kong 999077, Peoples R China.

通讯机构：

[Chao Yin] D

Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong 999077, China<&wdkj&>Guangdong Province Engineering Laboratory for Geographic Spatio-temporal Big Data, Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China<&wdkj&>Author to whom correspondence should be addressed.

语种：

英文

关键词：

architectural style classification;heritage preservation;cultural heritage;Chinese traditional settlements;convolutional neural networks;CNN interpretability;Grad-CAM;AutoAugment;deep learning

期刊：

Remote Sensing

ISSN：

2072-4292

年：

2022

卷：

期：

页码：

5250-

DOI：

10.3390/rs14205250

基金类别：

This research was supported by the National Natural Science Foundation of China (grant number 42001198 and 42071195), the Science and Technology Program of Guangdong (grant number 2021B1212100006), the GDAS’ Project of Science and Technology Development (grant number 2022GDASZH-2022010202 and 2020GDASYL-20200104004), and the “Digital Preservation and Innovative Technologies for the Culture of Traditional Villages and Towns” Open Fund of National-Local Joint Engineering Laboratory (grant number CTCZ19K04).

机构署名：

本校为第一机构

院系归属：

地理与旅游学院

摘要：

The classification of architectural style for Chinese traditional settlements (CTSs) has become a crucial task for developing and preserving settlements. Traditionally, the classification of CTSs primarily relies on manual work, which is inefficient and time consuming. Inspired by the tremendous success of deep learning (DL), some recent studies attempted to apply DL networks such as convolution neural networks (CNNs) to achieve automated classification of the architecture styles. However, these studies suffer overfitting problems of the CNNs, leading to inferior classification performance. Mo...

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Towards Classification of Architectural Styles of Chinese Traditional Settlements Using Deep Learning: A Dataset, a New Framework, and Its Interpretability

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