## 6 Discussion and Conclusions
### Conclusions  
This paper used TSV images with deep learning and transfer learning to construct a deep learning model, TBMask R-CNN, which automatically extracts Chinese traditional-style architecture from street view images and proposed TBVI and STBVI to quantify pedestrians' visual perceptions of Chinese traditional-style buildings. With the support of TBMask R-CNN, the images of the Chinese traditional-style buildings within the Fifth Ring Road of Beijing were automatically segmented, and then the TBVI was used to quantify the spatial distribution of perception of Chinese traditional-style buildings from the pedestrian's perspective.  
In general, the spatial distribution of the Chinese traditional-style buildings was characterized by aggregation within the Second Ring Road, with a higher concentration of buildings in the north than in the south, and dispersion between the Second Ring Road (excluding the Second Ring Road)and the Fifth Ring Road. The average value of the TBVI indicated that traditional-style buildings within Beijing's Fifth Ring Road need to be protected compared with other styles of buildings, and the buildings to be planned within the Third Ring Road should consider whether their style will contribute to the Beijing urban planning goals proposed in 2017. The Moran's I of the O-TBVI was lower than that of the F-TBVI, which indicated that the Chinese traditional-style buildings that pedestrians perceived from street view were mainly folk-style buildings. Our work could be useful for urban street management and planning, Specifically, (1) we propose a method that can automatically perceive the urban landscape on a large scale; (2) the proposed indicators can quantitatively evaluate the overall spatial distribution of the urban traditional style, and provide an overview of the spatial distribution of traditional urban features, which helps urban management and planning. For example, location planning issues: when planning such as hotels, shopping malls, public stadiums and other large facilities, considered traditional landscapes as their viewing points; (3) street view images are updated quickly, and our proposed method can be directly applied to new street view images to perceive the city scene, and can help evaluate urban renewal projects with historical street view images. The project's influence on the perception of traditional-style buildings can be easily clarified quantitatively by comparison of the TBVI and STBVI values calculated through street view images before and after the project. All these analyses could contribute to the realization of the Beijing urban planning goal of \"forming a landscape control area between the Second and Third Ring Road\", which was proposed by the Beijing urban master plan in 2017.  
In this experiment, the Chinese traditional-style building recognition model, TBMask R-CNN, had MAP of 0.8 for the verification set, but the building occlusion problem caused by trees and utility poles in the street view images affected the accuracy of the recognition and segmentation of the Chinese traditional-style building images. Therefore, strategies for dealing with occlusion and improving the accuracy of the recognition and segmentation requires further study.  
Conceptualization, T.P., L.Z. and C.S.; methodology, L.Z.; formal analysis, M.W.; data curation, X.W.; writing--original draft preparation, L.Z.; writing--review and editing, T.P., L.Z. and M.W.; visualization, S.G.; supervision, Y.C.; funding acquisition, T.P. and L.Z. All authors have read and agreed to the published version of the manuscript.  
This research was founded by the National Natural Science Foundation of China, grant number 41525004, 41421001, and 41877523, and supported by a grant from the State Key Laboratory of Resources and Environmental Information System, and the Science Foundation of China University of Petroleum, Beijing, Grant No. ZX20200100.  
Many thanks to the Tencent company for authorizing us to use Tencent street view pictures in this study.  
The authors declare no conflict of interest.