Abstract
There are increasingly large amounts of imagery and video collected from a variety of sensor modalities. Considering that each individual image may contain considerable amounts of information, the ability to interpret, understand and extract scene information is highly beneficial for many communities such as online social networking sites, intelligence agencies and companies dealing with large-scale data mining. In order to enable automated scene understanding, there is a need for an organizing principle to store, visualize and exploit the data. Three-dimensional geometry provides such an organizing principle as imagery and video have inherent 3D structure and can be associated with geographic coordinates. Imagery with geo-spatial information can be used to develop a common 3D world model representation that integrates data across a wide variety of sensor modalities. In this paper, we leverage multiple large geo-spatial databases to create a 3D world model and develop a hierarchical image geo-location framework using a coarse-to-fine approach to geo-locate a query image. Starting at the coarsest level, we developed a novel method to geo-locate images to regions of the world through a process of terrain classification. Next, we developed novel medium-scale and fine-scale localization steps to rule out most of the coarsely geo-located regions and determine candidate geo-locations with geo-positioning accuracy at a city level. Our method was demonstrated on a 6.5 million image database and shown to improve on current state of the art in the areas of both terrain classification and image geo-location.
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Vasile, A.N., Camps, O. (2013). Hierarchical Image Geo-location on a World-Wide Scale. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2013. Lecture Notes in Computer Science, vol 8034. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41939-3_26
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DOI: https://doi.org/10.1007/978-3-642-41939-3_26
Publisher Name: Springer, Berlin, Heidelberg
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