["Transactions in GIS, Volume 30, Issue 5, August 2026. ", "\nABSTRACT\nStreet View Images (SVIs) have become a valuable source of geotagged visual data, increasingly used in tasks such as assessing socioeconomic conditions, mapping accessibility, monitoring infrastructure, and supporting navigation and perception studies. However, while the spatial coverage and utility of SVIs are well recognized, their temporal variation and its impact on the generalisability and robustness of geospatial AI models remain underexplored. To address this gap, we examine how temporal bias in SVIs affects the performance of cross‐view geolocalization models. We construct a temporally diverse dataset of historical SVIs, spatially aligned with the CVUSA validation set, enabling controlled evaluation of temporal effects. We evaluate two state‐of‐the‐art deep learning models, TransGeo and SAFA, on this temporally diverse dataset, assessing retrieval accuracy under temporal shifts. To further explore how specific visual changes impact outcomes, we apply semantic segmentation to categorize scene types and use SHAP analysis to interpret how feature variations contribute to retrieval success. Our study establishes the importance of explicitly accounting for temporal diversity on semantic feature composition in the development and evaluation of geospatial AI methods. The results reveal a substantial decline in retrieval accuracies for both models when evaluated on the temporally diverse dataset. Semantic analysis further indicates that temporal shifts in visual features, particularly those related to urban infrastructure and natural landscape, can systematically influence model performance.\n"]