Research on the quantification of historical street space based on image semantic segmentation

Lina Yan; Yile Chen; Liang Zheng; Yi Zhang; Chun Zhu

doi:10.1117/12.2630998

18 March 2022 Research on the quantification of historical street space based on image semantic segmentation

Lina Yan, Yile Chen, Liang Zheng, Yi Zhang, Chun Zhu

Proceedings Volume 12168, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021); 1216814 (2022) https://doi.org/10.1117/12.2630998
Event: International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021), 2021, Harbin, China

Abstract

Different from the existing empirical landscape measurement methods, this article explores an effective method to quantify the landscape through the use of computer vision perception. First, based on image semantic segmentation technology, a large number of excellent case images are put into image semantic segmentation and annotation tools for image segmentation. Secondly, the image semantic segmentation is used to quantitatively analyze the landscape space elements of the old urban site, and obtain the data value of each landscape element in the excellent case, which provides a reference for the design of the element ratio data basis. Finally, it is applied to the street space in the old city of Macau to perform a quantitative analysis of the computer vision perception of the streets in the old city. Get quantitative suggestions for landscape improvement, and provide new methods for quantitative research and transformation of old urban areas.

Citation Download Citation

Lina Yan, Yile Chen, Liang Zheng, Yi Zhang, and Chun Zhu "Research on the quantification of historical street space based on image semantic segmentation", Proc. SPIE 12168, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021), 1216814 (18 March 2022); https://doi.org/10.1117/12.2630998

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