We introduce a novel architecture for near/remote sensing that is based on geospatial attention and demonstrate its use for five segmentation tasks.
We propose an end-to-end architecture for depth estimation that uses geospatial context to infer a synthetic ground-level depth map from a co-located overhead image, then fuses it inside of an encoder/decoder style segmentation network.
Finally, we demonstrate how the proposed method can be employed to estimate a possible time-of-capture in scenarios in which the timestamp is missing from the metadata.
Direct methods, such as PoseNet, regress pose from the image as a fixed function, for example using a feed-forward convolutional network.
A primary challenge in developing algorithms for identifying such artifacts is the cost of collecting annotated training data.
We propose to implicitly learn to extract geo-temporal image features, which are mid-level features related to when and where an image was captured, by explicitly optimizing for a set of location and time estimation tasks.
An emerging approach for conducting such assessments in the United States is through the US Road Assessment Program (usRAP), which rates roads from highest risk (1 star) to lowest (5 stars).
In this work, we propose a cross-view learning approach, in which images captured from a ground-level view are used as weakly supervised annotations for interpreting overhead imagery.
To evaluate our approach, we created a large dataset of overhead and ground-level images from a major urban area with three sets of labels: land use, building function, and building age.
While natural beauty is often considered a subjective property of images, in this paper, we take an objective approach and provide methods for quantifying and predicting the scenicness of an image.
We use our network to address the task of estimating the geolocation and geoorientation of a ground image.
Ranked #6 on Cross-View Image-to-Image Translation on cvusa
Our method reverses this process: we propose a set of horizon line candidates and score each based on the vanishing points it contains.
Ranked #2 on Horizon Line Estimation on York Urban Dataset
The horizon line is an important contextual attribute for a wide variety of image understanding tasks.
Ranked #2 on Horizon Line Estimation on Horizon Lines in the Wild
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 to georeferenced aerial images.