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GeoGNN introduces a two-tower architecture that significantly improves time series geolocalization by jointly modeling temporal patterns and spatial adjacency graphs. The method achieves a ~27% average accuracy boost on large-scale electricity consumption datasets, offering a robust baseline for location-aware applications. This approach effectively adapts image geolocalization concepts to sequential data, providing a strong foundation for future research in spatial-temporal modeling.
GeoGNN: Time Series Geo-Localization using Two-Tower Graph Neural Networks
arXiv:2606.08303v1 Announce Type: new Abstract: This paper investigates a novel concept of time series geolocalization, where the goal is to infer the geographic origin of each raw time series. Successful geolocalization can provide spatial context to time series, enabling downstream location-aware applications. We formalize the problem, adapt core ideas from image geolocalization to establish strong baselines, and propose GeoGNN, a two-tower architecture. During training, GeoGNN's spatial tower learns embeddings of geographic cell candidates by leveraging the geographic adjacency graph, while the temporal tower extracts informative representations from time series. During inference, each temporal representation is matched against candidate geographic embeddings using dot-product similarity, combined with an auxiliary classification head, to predict the time series' associated geographic origin. Experiments on large-scale, countrywide electricity-consumption datasets demonstrate that GeoGNN achieves the best performance across datasets and enhances both fine- and coarse-grained geolocalization accuracy by ~27% on average.
Abstract:This paper investigates a novel concept of time series geolocalization, where the goal is to infer the geographic origin of each raw time series. Successful geolocalization can provide spatial context to time series, enabling downstream location-aware applications. We formalize the problem, adapt core ideas from image geolocalization to establish strong baselines, and propose GeoGNN, a two-tower architecture. During training, GeoGNN's spatial tower learns embeddings of geographic cell candidates by leveraging the geographic adjacency graph, while the temporal tower extracts informative representations from time series. During inference, each temporal representation is matched against candidate geographic embeddings using dot-product similarity, combined with an auxiliary classification head, to predict the time series' associated geographic origin. Experiments on large-scale, countrywide electricity-consumption datasets demonstrate that GeoGNN achieves the best performance across datasets and enhances both fine- and coarse-grained geolocalization accuracy by ~27% on average.
Machine Learning (cs.LG)
arXiv:2606.08303 [cs.LG]
(or arXiv:2606.08303v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.08303 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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