ISPRS International Journal of Geo-Information, Volume 14, Issue 8
August 2025 - 39 articles
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Cover Story: In this study, we propose the Multi-Channel Spatio-Temporal Data Fusion (MCST-DF) framework, designed to integrate heterogeneous “big” and “small” data sources across complex road networks. Leveraging a novel Residual Spatio-Temporal Transformer Network (RSTTNet), our method captures both fine-grained local dynamics and global spatio-temporal patterns. By introducing multi-scale temporal channels and hierarchical spatial modelling, the framework effectively addresses challenges of data mismatch, sparsity, and heterogeneity. Evaluated on London traffic flow data, our approach achieves over 89% prediction accuracy and outperforms several strong baselines. This work contributes a generalisable solution to spatio-temporal data fusion, with wide implications for urban mobility, infrastructure monitoring, and geospatial AI systems. View this paper