A guided approach for cross-view geolocalization estimation with land cover semantic segmentation
Geolocalization is a crucial process that leverages environmental information and contextual data to accurately identify a position. In particular, cross-view geolocalization utilizes images from various perspectives, such as satellite and ground-level images, which are relevant for applications lik...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
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| Series: | Biomimetic Intelligence and Robotics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667379724000664 |
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| Summary: | Geolocalization is a crucial process that leverages environmental information and contextual data to accurately identify a position. In particular, cross-view geolocalization utilizes images from various perspectives, such as satellite and ground-level images, which are relevant for applications like robotics navigation and autonomous navigation. In this research, we propose a methodology that integrates cross-view geolocalization estimation with a land cover semantic segmentation map. Our solution demonstrates comparable performance to state-of-the-art methods, exhibiting enhanced stability and consistency regardless of the street view location or the dataset used. Additionally, our method generates a focused discrete probability distribution that acts as a heatmap. This heatmap effectively filters out incorrect and unlikely regions, enhancing the reliability of our estimations. Code is available at https://github.com/nathanxavier/CVSegGuide. |
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| ISSN: | 2667-3797 |