SenseGeo : A Cross‐View Geo‐Localization Method With Perspective‐Aware Encoding and Cross‐Regional Generalization

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ABSTRACT Cross‐view geolocation aims to localize a ground‐view query image by retrieving the best match from a database of geo‐tagged aerial‐view and satellite‐view images. The perspective gap and urban complexity lead to appearance variations and spatial misalignment between query and reference images, making the task challenging. Although many existing works perform well on single benchmarks, they often overfit dataset characteristics and fail to adapt to unseen regions. To address this problem, we propose SenseGeo, a cross‐view localization framework that improves generalization while maintaining accuracy. SenseGeo adopts a two‐phase training strategy consisting of Phase I and Phase II. Phase I performs supervised learning with global comparison and local constraints. Phase II, termed label expanded adaptation phase (LEAP), generates pseudo labels for new regions and alternates between global comparison and joint optimization with local constraints. We further introduce two modules within the framework. Slice‐guided perspective‐aware encoding (SPAE) extracts perspective‐aware local features from global representations, and position‐prior alignment with local enhancement (PALE) imposes positional consistency using multiple priors to alleviate cross‐perspective misalignment. Experiments show that SenseGeo achieves competitive performance within the same region and surpasses existing methods in cross‐region evaluation, demonstrating robust generalization for real‐world cross‐view geolocation.

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