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

TL;DR

This paper introduces an automatic, accurate, and efficient method for assessing window view indices in high-rise urban areas using 3D city models, significantly improving speed and precision over previous 2D approaches.

Contribution

It presents a novel workflow combining 3D semantic segmentation and batch computation to evaluate window views, enhancing large-scale urban view assessment accuracy and efficiency.

Findings

Estimated WVIs with RMSE < 0.01 demonstrate high accuracy.

Method is 3.68 times faster than previous 2D segmentation approaches.

Facilitates large-scale, automated window view assessments in dense urban environments.

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.