MT-GEO: A Multi-Scale Feature Extraction Network for Cross-View Geo-Localization Between Street-View and Remote Sensing Imagery

Cross-view geo-localization aims to determine the geographic origin of street-view images by matching them with a repository of remote sensing (RS) images equipped with GPS tags. Due to the substantial dissimilarities in viewpoint and visual appearance between street-view and RS images, this task is highly challenging. Recently, many deep-learning-based methods have been proposed. The CNN-based cross-view geo-localization techniques often employ polar transform and fail to capture extensive spatial correlations. And traditional transformer-based methods are prone to losing fine-grained details as a result of downsampling. Addressing these challenges, we propose a novel multi-scale transformer architecture for cross-view geo-localization in this study. Our model leverages multi-scale feature extraction to bolster the accuracy of image matching. We have conducted experiments on an open benchmark, demonstrating our approach’s superiority over the state-of-the-art methods.