AI-Based Identification and Redevelopment Prioritization of Inefficient Industrial Land Using Street View Imagery and Multi-Criteria Modeling

Yan Yu, Qiqi Yan, Yu Guo, Chenhe Zhang (), Zhixiang Huang and Liangze Lin
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Yan Yu: School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China
Qiqi Yan: School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China
Yu Guo: Zhejiang Space-Time Sophon Big Data Co., Ltd., Ningbo 315101, China
Chenhe Zhang: School of Resource and Environmental Science, Wuhan University, Wuhan 430070, China
Zhixiang Huang: School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China
Liangze Lin: School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China

Land, 2025, vol. 14, issue 6, 1-19

Abstract: The strategic prioritization of inefficient industrial land (IIL) redevelopment is critical for directing capital allocation toward sustainable urban regeneration. However, current redevelopment prioritization suffers from inefficient identification of IIL and ambiguous characterization of redevelopment potential, which hinders the efficiency of land resource allocation. To address these challenges, this study develops an AI-driven redevelopment prioritization framework for identifying IIL, evaluating redevelopment potential, and establishing implementation priorities. For land identification we propose an improved YOLOv11 model with an AdditiveBlock module to enhance feature extraction in complex street view scenes, achieving an 80.1% mAP on a self-built dataset of abandoned industrial buildings. On this basis, a redevelopment potential evaluation index system is constructed based on the necessity, maturity, and urgency of redevelopment, and the Particle Swarm Optimization-Projection Pursuit (PSO-PP) model is introduced to objectively evaluate redevelopment potential by adaptively reducing the reliance on expert judgment. Subsequently, the redevelopment priorities were classified according to the calculated potential values. The proposed framework is empirically tested in the central urban area of Ningbo City, China, where inefficient industrial land is successfully identified and redevelopment priority is categorized into near-term, medium-term, and long-term stages. Results show that the framework integrating computer vision and machine learning technology can effectively provide decision support for the redevelopment of IIL and offer a new method for promoting the smart growth of urban space.

Keywords: inefficient industrial land; redevelopment; street view images; redevelopment prioritization; potential evaluation (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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