Temporal Resilience in Geo-Localization: Adapting to the Continuous Evolution of Urban and Rural Environments

Static cross-view gee-localization datasets fail to capture the dynamic nature of real-world environments, as they do not account for rapid urban development and seasonal changes. As a result, models trained on such datasets experience degraded performance when confronted with more recent data, as they struggle to adapt to temporal variations such as newly constructed buildings or changing land-scapes. To accurately assess the performance gap and evaluate model robustness, it is essential to use temporally diverse data that allows us to measure how well models can handle temporal shifts and remain resilient to such changes. To address this need, we have enriched the CVUSA dataset with recent satellite and Street View imagery, creating the CVTemporal dataset. This enhanced dataset is critical for testing how well geo-localization models can adapt to temporal discrepancies and identify persistent, invariant features. In this work, we also examine the impact of temporal changes on the performance of selected well-known cross-view geo-localization models. Furthermore, we present a re-ranking approach based on existing satellite imagery from both datasets, which leads to significant performance improvements. Despite temporal variations in the data we achieve with our models remarkably good results especially on R@ 1. Additionally, we investigate strategies for identifying which temporally changed data should be collected to update pre-trained models, minimizing the labor-intensive process of recollecting entire datasets. Experiments with the CVUSA datasets demonstrate that it is possible to improve temporal alignment and model performance with only a small fraction of newly collected data.