Wide-Area Image Geolocalization with Aerial Reference Imagery

Understand

We propose to use deep convolutional neural networks to address the problem of cross-view image geolocalization, in which the geolocation of a ground-level query image is estimated by matching to georeferenced aerial images.

• We use state-of-the-art feature representations for ground-level images and introduce a cross-view training approach for learning a joint semantic feature representation for aerial images.
• We also propose a network architecture that fuses features extracted from aerial images at multiple spatial scales.
• To support training these networks, we introduce a massive database that contains pairs of aerial and ground-level images from across the United States.