Urban physical environment brings rich and vivid visual image to the residents, and plenty of quantitative research results have shown that the amenity is closely bound up with urban public well-being and health conditions. Landsenses Ecology provides a new guidance for exploring the connection between physical environment and residents' perception information, and interprets multi-dimensional senses such as vision, auditory and gustation in urban environment through quantitative measures in the human scale. This article follows the basic principles of Landsenses Ecology, introduces a city environment quantification method that combines a street view dataset and deep learning framework. Taking the Sixth Ring Road area of Beijing as an example, the Landsenses View Factors (LVFs) are used as metrics to explain the urban environment in a human-oriented perspective. While comprehensively controlling the multi-dimensional landsenses factors, our goal is to realize the people-oriented optimization design of urban physical environment, so as to meet the actual needs of residents to improve the quality of life. The experimental results show that: (1) from the perspective of visual perception at the macro level, the "closedness" of building environment within the Fourth Ring Road in Beijing is relatively strong, and the perceptibility of green view is relatively weak. It means that environmental designs in stock should be deployed and the components of visual interface should be optimized in the Fourth Ring Road area. (2) Clustering with the LVFs as the feature variables yields three types of dominant spaces (green space, gray space and blue space, separately). The vertical green infrastructures can be concentratedly deployed on the "gray space" to improve the perceptibility of urban green view at eye level, thereby creating a comfortable and pleasant green atmosphere, and promoting physical and mental health of the public. (3) This article provides data and method supplement based on big data thinking for Landsenses Ecology. In summary, this article analyzes the urban space of Beijing's central area from the perspective of Street view images (SVIs) and Landsenses Ecology, using the state-of-the-art deep learning framework named Detectron2 combined with a conventional machine learning model (K-Means clustering algorithm) to interpret the spatial distribution characteristics of the multiple LVFs in the humanistic perspective. Taking advantage of landsenses ecological planning, the perception quality of urban visual interface and the level of intelligent management can be improved, which helps the urban planners and managers to improve the quality and aesthetic of urban public environment from the human scale.