Algorithm for Identifying Micro-Update Potential in Urban Corner Spatial Environment Design by Integrating Multi-Source Data
DOI:
https://doi.org/10.67541/jdf2605Keywords:
Urban corner space; Multi-source data fusion; Cross-modal attention; Graph attention network; Micro update potential identificationAbstract
Aiming at the problems of multi‑source data heterogeneity, insufficient environmental representation, and insufficient spatial correlation description in the potential recognition of urban corner space micro-renewal, a potential recognition method based on multi‑source data and spatial relationship enhancement learning is proposed. By integrating remote sensing images, street view images, POI data, road network data, building form data, land use data, and population activity data, multi‑scale environmental features are constructed, and a cross‑modal attention mechanism and a multi‑relation graph attention network are introduced to realize heterogeneous feature interaction and neighborhood spatial dependence modeling. On 1,216 urban corner space samples, the accuracy, F1‑score, ROC‑AUC, and PR‑AUC of the proposed model reach 0.887, 0.881, 0.946 and 0.921 respectively, which is 3.3 percentage points higher than that of the standard GAT. Ablation, cross‑region migration, and perturbation experiments further verify the effectiveness, generalization ability and robustness of the model. This study can provide data-driven technical support for automatic screening of urban corner space, prioritization of micro-update and fine governance.
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Data Availability Statement
The data that support the findings of this study are available upon request from the corresponding authors, J.Z.
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Copyright (c) 2026 Jing Zhang (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
