Multiscale Scoring Model for Enhanced Urban Perception Evaluation

Abstract

Effective urban management, renewal, and development rely on identifying low-quality areas within the city. However, previous studies have been limited by low-volume handcraft surveys and a dearth of data sources, making it difficult to understand human perception within the urban environment. In this paper, we propose a powerful yet simple scoring model to perform street view image recognition and evaluation which utilizes both global information and feature-level semantic information of street elements, resulting in a high-precision perception model on 6 indexes (Beautiful, Lively, Safe, Wealthy, Boring, and Depressing) from Place Pulse 2.0 dataset. The model is then independently applied to a large-scale and fine-grained evaluation task of 4,384 street view images in Shameen Region, Guangzhou, providing valuable perception details and decision-making support for urban planning for the local government. We believe our work will accelerate the digitization and intelligent transformation of municipal engineering.