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The detection of deformation in Global Navigation Satellite System (GNSS) time series associated with (a)seismic events down to a low magnitude is still a challenging issue. The presence of a considerable amount of noise in the data makes it difficult to reveal patterns of small ground deformation. Traditional analyses and methodologies are able to effectively retrieve the deformation associated with medium to large magnitude events. However, the automatic detection and characterization of such events is still a complex task, because traditionally employed methods often separate the time series analysis from the source characterization. Here we propose a first end‐to‐end framework to characterize seismic sources using geodetic data by means of deep learning, which can be an efficient alternative to the traditional workflow, possibly overcoming its performance. We exploit three different geodetic data representations in order to leverage the intrinsic spatio‐temporal structure of the GNSS noise and the target signal associated with (slow) earthquake deformation. We employ time series, images, and image time series to account for the temporal, spatial, and spatio‐temporal domain, respectively. Thereafter, we design and develop a specific deep learning model for each dataset. We analyze the performance of the tested models both on synthetic and real data from North Japan, showing that image time series of geodetic deformation can be an effective data representation to embed the spatio‐temporal evolution, with the associated deep learning method outperforming the other two. Therefore, jointly accounting for the spatial and temporal evolution may be the key to effectively detect and characterize fast or slow earthquakes. Plain Language Summary: The continuous monitoring of ground displacement with Global Navigation Satellite System allowed, at the beginning of the 2000s, the discovery of slow earthquakes—a transient slow slippage of tectonic faults that releases stress without generating seismic waves. Nevertheless, the detection of small events is still a challenge, because they are hidden in the noise. Most of the methods which are traditionally employed are able to extract the deformation down to a certain signal‐to‐noise level. However, one can ask if deep learning can be a more efficient and powerful alternative. To this end, we address the problem by using deep learning, as it stands as a powerful way to automatize and possibly overcome traditional methods. We use and compare three data representations, that is time series, images, and image time series of deformation, which account for the temporal, spatial, and spatio‐temporal variability, respectively. We train our methods on synthetic data, since real datasets are still not enough to be effectively employed with deep learning, and we test on synthetic and real data as well, claiming that image time series and its associated deep learning model may be more effective toward the study of the slow deformation. Key Points: We develop deep learning approaches on synthetics mimicking the spatio‐temporal structure of static deformation and realistic Global Navigation Satellite System (GNSS) noiseWe design three deep learning models and we train and test them against three GNSS data representationsTransformers and image time series of deformation can effectively characterize small deformation patterns associated with the seismic source [ABSTRACT FROM AUTHOR]