Efficient and accurate assessment of window view distance using City Information Models and 3D Computer Vision

Abstract

A distant view through a window is preferred by urban dwellers due to its benefits to human health and well-being. A high window view distance is also valued in real estate markets, especially in high-rise, high-density urban areas. Thus, an urban-scale assessment of window view distance is significant in examining the disparity of sharing of view openness for applications and analytics in urban health, planning and design, and housing. However, current limited assessment methods are neither accurate nor efficient. The evolving photorealistic City Information Models (CIMs) and 3D Computer Vision (CV) enable a new solution due to the high-resolution and efficient semantic representation of urban landscapes. This study aims to present a window view distance index (WVDI) together with an accurate and efficient assessment method using up-to-date 3D CIMs and CV. First, we define the WVDI on a CIM-generated window view image considering the visual permeability of greenery. Then, an automatic assessment of WVDIs is designed on a type-depth window view using 3D semantic segmentation and OpenGL rendering. Experimental tests in Hong Kong Island and Kowloon Peninsula of Hong Kong confirmed that our method was i) accurate for both non-greenery views (RMSD ≤ 0.0002) and greenery views (RMSD ≤ 0.1781) and ii) improved the efficiency of the traditional visibility analysis-based method by 99.96 %. The proposed approach can support multiple urban applications, e.g., prioritized improvement of visual urban density, overall optimization of window view distance for architectural and urban design, and precise housing valuation and transaction.