A guided approach for cross-view geolocalization estimation with land cover semantic segmentation

A guided approach for cross-view geolocalization estimation with land cover semantic segmentation

Blog Article

Geolocalization is a crucial process that leverages environmental information and contextual data to accurately identify a position.In particular, Knee Walkers 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 LIPO-FLAVONOID 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.