Hashing for Geo-Localization

In this article, we undertake the task of fast geo-localization of a query ground image using geo-tagged aerial images. To this end, we propose a hashing strategy that fast searches the database of geo-tagged aerial images for the ground image's matches, whose geo-tags are exploited to estimate the ground geographic location. Specifically, we commence by converting the aerial images into ground-view aerial images that have the common angle of view (i.e., horizontal view) with the ground image. We then develop a feature extraction model and a hash encoder for generating hash codes for the images. Based on these models, the ground image and the geo-tagged aerial images are transformed to hash codes that comprehensively reflect their visual content similarity. Fast searching the geo-tagged aerial image database for the ground image's matches is conducted subject to small Hamming distance between the hash codes. We extract a geographical cluster from the matched aerial images subject to their geo-tags. In this way, the geographic location of the ground image is efficiently retrieved according to the geographical cluster. Experiments on two datasets validate the efficiency and effectiveness of our proposed framework. We have released our implementation code at https://github.com/taoyiminR/Hashing_for_geo-localization for public evaluation.