Spatiotemporal Coupling Analysis of Street Vitality and Built Environment: A Multisource Data-Driven Dynamic Assessment Model

Caijian Hua (), Wei Lv and Yan Zhang
Additional contact information
Caijian Hua: School of Computer Science and Engineering, Sichuan University of Science and Engineering, Yibin 644000, China
Wei Lv: School of Computer Science and Engineering, Sichuan University of Science and Engineering, Yibin 644000, China
Yan Zhang: School of Computer Science and Engineering, Sichuan University of Science and Engineering, Yibin 644000, China

Sustainability, 2025, vol. 17, issue 21, 1-18

Abstract: To overcome the limited accuracy of existing street vitality assessments under dense occlusion and their lack of dynamic, multi-source data fusion, this study proposes an integrated dynamic model that couples an enhanced YOLOv11 with heterogeneous spatiotemporal datasets. The network introduces a two-backbone architecture for stronger multi-scale fusion, Spatial Pyramid Depth Convolution (SPDConv) for richer urban scene features, and Dynamic Sparse Sampling (DySample) for robust occlusion handling. Validated in Yibin, the model achieves 90.4% precision, 67.3% recall, and 77.2% mAP@50 gains of 6.5%, 5.3%, and 5.1% over the baseline. By fusing Baidu heatmaps, street-view imagery, road networks, and POI data, a spatial coupling framework quantifies the interplay between commercial facilities and street vitality, enabling dynamic assessment of urban dynamics based on multi-source data fusion, offering insights for targeted retail regulation and adaptive traffic management. By enabling continuous monitoring of urban space use, the model enhances the allocation of public resources and cuts energy waste from idle traffic, thereby advancing urban sustainability via improved commercial planning and responsive traffic control. The work provides a methodological foundation for shifting urban resource allocation from static planning to dynamic, responsive systems.

Keywords: street vitality; YOLOv11; street view image; pedestrian detection; multi-source data fusion; sustainability (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/21/9517/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/21/9517/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:21:p:9517-:d:1779970

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().