Cross View Geo Localization

Cross-view geo-localization aims to pinpoint the geographic location of an image by matching it to a geotagged image from a different perspective (e.g., ground vs. aerial views), often in challenging conditions like varying weather or limited field of view. Current research heavily utilizes deep learning, particularly convolutional neural networks (CNNs) and graph neural networks (GNNs), often incorporating techniques like bird's-eye-view transformations and contrastive learning to improve matching accuracy and robustness to viewpoint changes and image variations. This field is significant for applications requiring location awareness in GPS-denied environments, such as autonomous navigation and robotics, and advancements are driving improvements in image retrieval and representation learning.