Efficient assessment of window views in high-rise, high-density urban areas using 3D color City Information Models

Abstract

Urban-scale quantification of window views can inform housing selection and valuation, landscape management, and urban planning. However, window views are numerous in high-rise, high-density urban areas and current automatic assessments of window views are inaccurate and time-consuming. Thus, both accurate and efficient assessment of window views is significant in improving the automation for urban-scale window view applications. The paper presents an automatic, accurate, and efficient assessment of window view indices (WVIs) of greenery, sky, waterbody, and construction using 3D color City Information Models (CIMs). The workflow includes: i) 3D semantic segmentation of photorealistic CIM and Digital Surface Model (DSM), and ii) batch computation of WVIs. Experimental results showed the estimated WVIs were more accurate (RMSE < 0.01), and the proposed method was more efficient (3.68 times faster) than Li et al.'s (2022) 2D semantic segmentation. Thus, the proposed method can facilitate large-scale WVI assessment and update in healthy high-rise, high-density urban development.