Cross-View Image Geo-localization

Chapter

Abstract

The recent availability of large amounts of geo-tagged imagery has inspired a number of data-driven solutions to the image geo-localization problem. Existing approaches predict the location of a query image by matching it to a database of geo-referenced photographs. While there are many geo-tagged images available on photo sharing and Street View sites, most are clustered around landmarks and urban areas. The vast majority of the Earth’s land area has no ground-level reference photos available, which limits the applicability of all existing image geo-localization methods. On the other hand, there is no shortage of visual and geographic data that densely covers the Earth—we examine overhead imagery and land cover survey data—but the relationship between this data and ground-level query photographs is complex. In this chapter, we introduce a cross-view feature translation approach to greatly extend the reach of image geo-localization methods. We can often localize a query even if it has no corresponding ground-level images in the database. A key idea is to learn a mapping from ground-level appearance to overhead appearance and land cover attributes. This relationship is learned from sparsely available geo-tagged ground-level images and the corresponding aerial and land cover data at those locations. We perform experiments over a 1135 km\(^2\) region containing a variety of scenes and land cover types. For each query, our algorithm produces a probability density over the region of interest.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Cornell UniversityIthacaUSA
  2. 2.Brown UniversityProvidenceUSA

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