Evaluation and Optimization of Urban Street Spatial Quality Based on Street View Images and Machine Learning: A Case Study of the Jinan Old City
As one of the most important urban public spaces, the design and management of streets have shifted from “two-dimensional plan” to “three-dimensional space”, and higher requirements have been put forward for the scale and precision of urban design. The core research question of this research is how...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
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| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/15/9/1408 |
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| Summary: | As one of the most important urban public spaces, the design and management of streets have shifted from “two-dimensional plan” to “three-dimensional space”, and higher requirements have been put forward for the scale and precision of urban design. The core research question of this research is how to refine street spatial quality measurement and evaluation based on multitemporal street view images, while providing basic data and corresponding decision support for updates and renovations. “One Garden and Twelve Fangs” in Jinan old city is the core area of the Jinan Commercial Port District. It integrates diverse cultural elements of tradition and modernity, local and foreign, and is of great significance to the cultural inheritance and urban development of Jinan. Nowadays, there is a lack of vitality, lagging development, and shorting of high-quality living service facilities here. How to enhance the overall vitality of the region and drive regional social value is an urgent problem that needs to be solved at present. This research takes the old city area of Jinan as the research scope, constructs a street space quality evaluation model through street view images and machine learning, and establishes the connection between quantitative research on street space quality and urban renewal practice. In this research, the standard system will be supplemented and improved, and the practicality of the application will be enhanced through more refined evaluation models. The evaluation indicators include walkability, green visibility, enclosure, openness, imaginability, coordination, extreme boundary area, and interface transparency. This article provides a feasible framework and paradigm for measuring the quality of large-scale and high-precision street spaces through the combination of big data and artificial intelligence, effectively bridging the gap between spatial quantification research and urban renewal practices. |
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| ISSN: | 2075-5309 |