Related papers: GeoGNN: Time Series Geo-Localization using Two-Tower Graph Neural Networks

GeoGNN: Time Series Geo-Localization using Two-Tower Graph Neural Networks

URL: http://arxiv.org/abs/2606.08303v1
Date: Sat, 06 Jun 2026 19:10:56 GMT
Title: GeoGNN: Time Series Geo-Localization using Two-Tower Graph Neural Networks
Abstract summary: Successful geolocalization can provide spatial context to time series, enabling location-aware applications.<n>We propose GeoGNN, a two-tower architecture for time series geolocalization.<n>Experiments on large-scale, countrywide electricity-consumption datasets demonstrate that GeoGNN achieves the best performance across datasets.
Score: 11.28706377321882
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

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