A data-driven method for feature assessment of historical settlements: A case study of Northeast Hubei, China

被引:1
|
作者
Tan, Gangyi [1 ,2 ,3 ,4 ]
Chen, Zhanxiang [1 ,2 ,3 ,4 ]
Zhu, Jiangkun [1 ,2 ,3 ,4 ]
Wang, Kai [1 ,2 ,3 ,4 ]
机构
[1] Huazhong Univ Sci & Technol, Sch Architecture & Urban Planning, Wuhan 430074, Peoples R China
[2] Hubei Engn & Technol Res Ctr Urbanizat, Wuhan 430074, Peoples R China
[3] Hubei Rural Construct Ctr, Wuhan 430074, Peoples R China
[4] HUST Built Heritage Res Ctr, Wuhan 430074, Peoples R China
基金
中国国家自然科学基金;
关键词
Historical settlement; Feature assessment; Data-driven method; Northeast Hubei; AUTHENTICITY;
D O I
10.1016/j.foar.2023.12.005
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Formulating criteria for the assessment system of historic settlements is challenging due to complex geographical conditions and evaluator knowledge limitations, leading to subjective bias in the assessment process. To address this issue, this study proposes a datadriven method for assessing the features of historical settlements to carry out scientific and refined assessment and result analysis. Focusing on Northeast Hubei as the study area, this paper selects 3 historical settlements for validation and analysis. The results of the study show that (1) the data-driven method expands the methodological chain of assessing historical settlement features, and improves the assessment efficiency and scientificity of the assessment results by applying it to the new assessment process; (2) Through comparing the assessment results of the validation cases and data samples, the study establishes a comprehensive quantitative ranking of the assessment of historical settlement features and identifies the main influencing factors, thus enhancing the precision of result analysis; (3) By comparing the resulting assessment framework with the current assessment system, this study confirms the advantages of the proposed framework in identifying nuanced features and aligning with geographical conditions, thereby verifying the effectiveness of the data-driven method. (c) 2023 The Author(s). Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).
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页码:387 / 405
页数:19
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