Monitoring the green evolution of vernacular buildings based on deep learning and multi-temporal remote sensing images

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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：

Wen, Baohua;Peng, Fan;Yang, Qingxin;Lu, Ting;Bai, Beifang;...

通讯作者：

Xu, Feng(fengxu@hnu.edu.cn)

作者机构：

[Xu, Feng; Peng, Fan; Wen, Baohua; Yang, Qingxin] Hunan Univ, Sch Architecture & Planning, Changsha 410082, Hunan, Peoples R China.

[Xu, Feng; Wen, Baohua] Hunan Key Lab Sci Urban & Rural Human Settlements, Changsha 410082, Hunan, Peoples R China.

[Bai, Beifang; Lu, Ting] Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China.

[Wu, Shihai] Changsha Univ Sci & Technol, Sch Architecture, Changsha 410004, Hunan, Peoples R China.

通讯机构：

[Feng Xu] S

School of Architecture and Planning, Hunan University, Changsha, China<&wdkj&>Hunan Key Laboratory of Sciences of Urban and Rural Human Settlements at Hilly Areas, Changsha, China

语种：

英文

关键词：

courtyard buildings;evolution;deep learning;high-resolution network;remote sensing images

期刊：

建筑模拟(英文)

ISSN：

1996-3599

年：

2023

卷：

期：

页码：

151-168

DOI：

10.1007/s12273-022-0927-7

基金类别：

This work was supported by National Natural Science Foundation of China (No. 52108010).

机构署名：

本校为其他机构

院系归属：

建筑学院

摘要：

The increasingly mature computer vision (CV) technology represented by convolutional neural networks (CNN) and available high-resolution remote sensing images (HR-RSIs) provide opportunities to accurately measure the evolution of natural and artificial environments on Earth at a large scale. Based on the advanced CNN method high-resolution net (HRNet) and multi-temporal HR-RSIs, a framework is proposed for monitoring a green evolution of courtyard buildings characterized by their courtyards being roofed (CBR). The proposed framework consists of...

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Monitoring the green evolution of vernacular buildings based on deep learning and multi-temporal remote sensing images

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