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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 and ground-level images in an end-toend trainable neural network, which uses kernel regression and density estimation to convert features extracted from the ground-level images into a dense feature map. The output...
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) semantic features automatically extracted from co-located ground imagery. Our network architecture takes an aerial image as input, extracts features using a convolutional neural network, and then applies an adaptive...
Billions of geotagged ground-level images are available via social networks and Google Street View. Recent work in computer vision has explored how these images could serve as a resource for understanding our world. However, most ground-level images are captured in cities and around famous landmarks; there are still very large geographic regions with few images. This leads to artifacts when estimating...
We address the problem of single-image geo-calibration, in which an estimate of the geographic location, viewing direction and field of view is sought for the camera that captured an image. The dominant approach to this problem is to match features of the query image, using color and texture, against a reference database of nearby ground imagery. However, this fails when such imagery is not available...
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 candidate vanishing points, then remove outliers by enforcing mutual orthogonality. Our method reverses this process: we propose a set of horizon line candidates and score each based on the vanishing points it contains...
Given an image, we propose to use the appearance of people in the scene to estimate when the picture was taken. There are a wide variety of cues that can be used to address this problem. Most previous work has focused on low-level image features, such as color and vignetting. Recent work on image dating has used more semantic cues, such as the appearance of automobiles and buildings. We extend this...
We propose the use of deep convolutional neural networks to estimate the transient attributes of a scene from a single image. Transient scene attributes describe both the objective conditions, such as the weather, time of day, and the season, and subjective properties of a scene, such as whether or not the scene seems busy. Recently, convolutional neural networks have been used to achieve state-of-the-art...
Estimating the focal length of an image is an important preprocessing step for many applications. Despite this, existing methods for single-view focal length estimation are limited in that they require particular geometric calibration objects, such as orthogonal vanishing points, co-planar circles, or a calibration grid, to occur in the field of view. In this work, we explore the application of a...
Principal component analysis (PCA) is a widely used technique for dimensionality reduction which assumes that the input data can be represented as a collection of fixed-length vectors. Many real-world datasets, such as those constructed from Internet photo collections, do not satisfy this assumption. A natural approach to addressing this problem is to first coerce all input data to a fixed size, and...
Principal component analysis (PCA) is used in diverse settings for dimensionality reduction. If data elements are all the same size, there are many approaches to estimating the PCA decomposition of the dataset. However, many datasets contain elements of different sizes that must be coerced into a fixed size before analysis. Such approaches introduce errors into the resulting PCA decomposition. We...
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