Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/328337 
Year of Publication: 
2024
Citation: 
[Journal:] International Journal of Applied Earth Observation and Geoinformation [ISSN:] 1872-826X [Volume:] 129 [Article No.:] 103865 [Publisher:] Elsevier BV [Place:] Amsterdam [Year:] 2024
Publisher: 
Elsevier BV, Amsterdam
Abstract: 
This paper presents a framework for interpreting regional features of houses in the Tibetan-Qiang region by Deep Learning (DL) and Image Landscape (IL), which learns the typical features from online building photos in different subordinate areas of the whole region through a set of datasets and DL models. The contribution of this framework is taking online building images as a proxy of rural building characteristics, which significantly improves the scope and efficiency of related built heritage studies and accurately reveals the representative features of houses in remote rural areas. The results are validated by established studies, and the framework can be transferred to other regions through the provided path and openly published datasets.
Subjects: 
Northwest Sichuan
Regional characteristic
House
Image Landscape
Deep Learning
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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