Cheng, Tao; Chen, Hao; Zhang, Xianghui; Gao, Xiaowei; Yin, Lu; Jiao, Jianbin; (2025) Multi-Channel Spatio-Temporal Data Fusion of ‘Big’ and ‘Small’ Network Data Using Transformer Networks. ISPRS International Journal of Geo-Information , 14 (8) , Article 286. 10.3390/ijgi14080286.

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Abstract

The integration of heterogeneous spatio-temporal datasets presents a critical challenge in geospatial data science, particularly when combining large-scale, passively collected “big” data with precise but sparse “small” data. In this study, we propose a novel framework—Multi-Channel Spatio-Temporal Data Fusion (MCST-DF)—that leverages transformer-based deep learning to fuse these data sources for accurate network flow estimation. Our approach introduces a Residual Spatio-Temporal Transformer Network (RSTTNet), equipped with a layered attention mechanism and multi-scale embedding architecture to capture both local and global dependencies across space and time. We evaluate the framework using real-world mobile sensing and loop detector data from the London road network, demonstrating over 89% prediction accuracy and outperforming several benchmark deep learning models. This work provides a generalisable solution for spatio-temporal fusion of diverse geospatial data sources and has direct relevance to smart mobility, urban infrastructure monitoring, and the development of spatially informed AI systems.

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