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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.
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.
Interpreting regional characteristics of Tibetan-Qiang houses in Northwestern Sichuan by Deep Learning and Image Landscape
2024
Article (Journal)
Electronic Resource
Unknown
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