Algorithm for Identifying Micro-Update Potential in Urban Corner Spatial Environment Design by Integrating Multi-Source Data

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.

Chen, L., Cheng, Y., Zhou, Z., & Wen, Y. (2025). The Impact of Differences in Renovation Models of Abandoned Boiler Rooms on Community Vitality—A Case Study of Shenyang, China. Buildings, 15(11), 1807. DOI: 10.3390/buildings15111807
Álvarez-Martínez, J. M., Lugonja, T. N., Valdés, A., Le Barbier, J. G., Suárez, M. P., Romero, G. H., ... & Jiménez-Alfaro, B. (2025). Four decades of remote sensing for monitoring terrestrial ecosystems: A global review and future challenges. Science of Remote Sensing, 100341. DOI: 10.1016/j.srs.2025.100341
Zhou, Z., Deng, F., Nie, J., Che, F., Guo, Y., & Wang, S. (2026). Research Progress in Algal Bloom Early Warning Technologies for Lakes: Methodological Evolution, Framework Development, and Adaptation to Cold and Arid Region Lakes. Applied Sciences, 16(15), 7469. DOI: 10.3390/app16157469
Zhao, T., Chen, G., Pang, C., Seenoi, P., Papukdee, N., & Busababodhin, P. (2025). Hybrid convolutional neural network-graph attention network-gradient boosting decision tree model for seismic impedance inversion prediction. Journal of Seismic Exploration, 34(5), 81-98. DOI: 10.36922/JSE025310051
Zhao, T., Chen, G., Suraphee, S., Phoophiwfa, T., & Busababodhin, P. (2025). A hybrid TCN-XGBoost model for agricultural product market price forecasting. PLoS One, 20(5), e0322496. DOI: 10.1371/journal.pone.0322496
Qutaishat, D., & Li, S. (2025). Multimodal Spatiotemporal Deep Fusion for Highway Traffic Accident Prediction in Toronto: A Case Study and Roadmap. ISPRS International Journal of Geo-Information, 14(11), 434. DOI: 10.3390/ijgi14110434
Zhao, T., Chen, G., & Busababodhin, P. (2026). An intelligent expert system for logistics disruption prediction and mitigation in global supply chains: Integrating graph-based risk inference with ensemble forecasting. Scientific Reports. DOI: 10.1038/s41598-026-61617-0
Sowmya, K., & Basthikodi, M. (2026). CGP-Net: A Cross-Modal Attention Network with Graph Priors for Integrated Multi-Omics Disease Subtyping. International Journal of Computer Information Systems and Industrial Management Applications, 18(14s), 401-415. DOI: 10.70917/ijcisim-2026-4239
Zhao, T., Chen, G., Pang, C., Li, L., & Busababodhin, P. (2026). Forecasting global agricultural trade imbalances using a hybrid deep learning and gradient boosting framework. Discover Computing, 29(1), 443. DOI: 10.1007/s10791-026-10367-8
Wang, M., Zhang, Q., Liu, X., Zhang, J., Yu, F., Zhang, X., & Zhao, R. (2025). Research progress on multimodal data fusion in forest resource monitoring. Frontiers in Plant Science, 16, 1710618. DOI: 10.3389/fpls.2025.1710618
Zhao, T., Chen, G., Li, J., Pang, C., Seenoi, P., Papukdee, N., & Busababodhin, P. (2026). XGBoost-Deep Residual (FedXGB-ResNet) collaborative porosity prediction in a federated learning framework. Journal of Seismic Exploration, 35(2), 201. DOI: 10.36922/JSE025440094
Zhao, T., Chen, G., Pang, C., Seenoi, P., Papukdee, N., Busababodhin, P., & Du, Y. (2025). Hybrid convolutional neural network-graph attention network-gradient boosting decision tree model for seismic impedance inversion prediction. Journal of Seismic Exploration, 34(5), 81-98. DOI: 10.36922/JSE025310051
Meyer, H., Reudenbach, C., Wöllauer, S., & Nauss, T. (2019). Importance of spatial predictor variable selection in machine learning applications–Moving from data reproduction to spatial prediction. Ecological Modelling, 411, 108815. DOI: 10.1016/j.ecolmodel.2019.108815
Zhao, T., Chen, G., Pang, C., Seenoi, P., Papukdee, N., & Busababodhin, P. (2025). Time-lapse earthquake difference prediction based on physics-informed long short-term memory coupled with interpretability boosting. Journal of Seismic Exploration, 34(3), 25. DOI: 10.36922/JSE025310049
Zhang, L., Liang, T., Wei, X., & Wang, H. (2024). An improved indicator standardization method for multi-indicator composite evaluation: A case study in the evaluation of ecological civilization construction in China. Environmental Impact Assessment Review, 108, 107600. DOI: 10.1016/j.eiar.2024.107600
Mukhametzyanov, I. (2023). On the conformity of scales of multidimensional normalization: An application for the problems of decision making. Decision Making: Applications in Management and Engineering, 6(1), 399-341. DOI: 10.31181/dmame05012023i
Kuang, W. (2019). Mapping global impervious surface area and green space within urban environments. Science China Earth Sciences, 62(10), 1591-1606. DOI: 10.1007/s11430-018-9342-3
Abass, K., Buor, D., Afriyie, K., Dumedah, G., Segbefi, A. Y., Guodaar, L., ... & Gyasi, R. M. (2020). Urban sprawl and green space depletion: Implications for flood incidence in Kumasi, Ghana. International Journal of Disaster Risk Reduction, 51, 101915. DOI: 10.1016/j.ijdrr.2020.101915
Liang, W., Ahmad, Y., & Mohidin, H. H. B. (2024). Spatial pattern and influencing factors of tourism based on POI data in Chengdu, China. Environment, Development and Sustainability, 26(4), 10127-10143. DOI: 10.1007/s10668-023-03138-8
Lu, C., Pang, M., Zhang, Y., Li, H., Lu, C., Tang, X., & Cheng, W. (2020). Mapping urban spatial structure based on POI (point of interest) data: A case study of the central city of Lanzhou, China. ISPRS International Journal of Geo-Information, 9(2), 92. DOI: 10.3390/ijgi9020092
Izadi, M., & Saeedi, P. (2012). Robust weighted graph transformation matching for rigid and nonrigid image registration. IEEE Transactions on Image Processing, 21(10), 4369-4382. DOI: 10.1109/TIP.2012.2208980
Yao, Y., Li, X., Liu, X., Liu, P., Liang, Z., Zhang, J., & Mai, K. (2017). Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model. International Journal of Geographical Information Science, 31(4), 825-848. DOI: 10.1080/13658816.2016.1244608
Qin, Q., Xu, S., Du, M., & Li, S. (2022). Identifying urban functional zones by capturing multi-spatial distribution patterns of points of interest. International Journal of Digital Earth, 15(1), 2468-2494. DOI: 10.1080/17538947.2022.2160841
Xing, R., Cheng, R., Huang, J., Li, Q., & Zhao, J. (2023). Learning to understand the vague graph for stock prediction with momentum spillovers. IEEE Transactions on Knowledge and Data Engineering, 36(4), 1698-1712. DOI: 10.1109/TKDE.2023.3310592
Zhang, L., Lin, G., Wei, L., & Kou, Y. (2024). Feature subset selection for multi-scale neighborhood decision information system via mutual information. Artificial Intelligence Review, 57(1), 15. DOI: 10.1007/s10462-023-10626-w