Previous studies on cross-view geolocalization have primarily focused on determining whether a query image accurately corresponds to a specific geographic location within a predefined gallery. However, this research paradigm often overlooks the extensive multiscale structural information inherent in the geographic space. To achieve more robust localization, a model must not only capture local architectural details but also understand the spatial relationships among targets reflected through building clusters and environmental features, thereby improving the localization accuracy across different spatial scales. To address these challenges, a multiscale cross-view geo-localization task is proposed and a Multi-Level Campus (ML-Campus) dataset is constructed specifically for this task. The ML-Campus dataset comprises multiview, multisource building images, each annotated with multiscale labels to reflect correlations and continuity across different spatial scales. Based on this dataset, an empirical evaluation of existing cross-view geo-localization methods is conducted, which is used as a benchmark to measure their performance in this context. To further enhance model performance, the proposed Cross-View HAPPIER (CV-HAPPIER) method is employed for training, which strengthens the model's feature representation capabilities across different spatial scales. Extensive experimental results on the ML-Campus dataset demonstrate that the CV-HAPPIER method significantly improves the spatial robustness of cross-view geo-localization retrieval ranking results.
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