A room with a view: Automatic assessment of window views for high-rise high-density areas using City Information Models and deep transfer learning

Abstract

Every windowed room has a view, which reflects the visibility of nature and landscape and has a strong influence on the health, living satisfaction, and housing value of inhabitants. Thus, automatic accurate window view assessment is vital in examining neighborhood landscape and optimizing the social and physical settings for sustainable urban development. However, existing methods are labor-intensive, inaccurate, and non-scalable to assess window views in high-rise, high-density cities. This study aims to assess Window View Indices (WVIs) quantitatively and automatically by using a photo-realistic City Information Model (CIM). First, we define four WVIs to represent the outside (i) greenery, (ii) water-body, (iii) sky, and (iv) construction views quantitatively. Then, we propose a deep transfer learning method to estimate the WVIs for the window views captured in the CIM. Preliminary experimental tests in Wan Chai District, Hong Kong confirmed that our method was highly satisfactory (R2 > 0.95) and fast (3.08 s per view), and the WVIs were accurate (RMSE < 0.042). The proposed approach can be used in computing city-scale window views for landscape management, sustainable urban planning and design, and real estate valuation.