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Wide-Area Image Geolocalization with Aerial Reference Imagery
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 toExpand
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Predicting Ground-Level Scene Layout from Aerial Imagery
We introduce a novel strategy for learning to extract semantically meaningful features from aerial imagery. Instead of manually labeling the aerial imagery, we propose to predict (noisy) semanticExpand
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Consistent Temporal Variations in Many Outdoor Scenes
This paper details an empirical study of large image sets taken by static cameras. These images have consistent correlations over the entire image and over time scales of days to months. SimpleExpand
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Revisiting IM2GPS in the Deep Learning Era
Image geolocalization, inferring the geographic location of an image, is a challenging computer vision problem with many potential applications. The recent state-of-the-art approach to this problemExpand
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Horizon Lines in the Wild
The horizon line is an important contextual attribute for a wide variety of image understanding tasks. As such, many methods have been proposed to estimate its location from a single image. TheseExpand
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Detecting Vanishing Points Using Global Image Context in a Non-ManhattanWorld
We propose a novel method for detecting horizontal vanishing points and the zenith vanishing point in man-made environments. The dominant trend in existing methods is to first find candidateExpand
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The global network of outdoor webcams: properties and applications
There are thousands of outdoor webcams which offer live images freely over the Internet. We report on methods for discovering and organizing this already existing and massively distributed globalExpand
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Sky segmentation in the wild: An empirical study
Automatically determining which pixels in an image view the sky, the problem of sky segmentation, is a critical preprocessing step for a wide variety of outdoor image interpretation problems,Expand
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A Unified Model for Near and Remote Sensing
We propose a novel convolutional neural network architecture for estimating geospatial functions such as population density, land cover, or land use. In our approach, we combine overhead andExpand
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Adventures in archiving and using three years of webcam images
Recent descriptions of algorithms applied to images archived from webcams tend to underplay the challenges in working with large data sets acquired from uncontrolled webcams in real environments. InExpand
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