A Unified Model for Near and Remote Sensing

Scott Workman, Menghua Zhai, David J. Crandall, Nathan Jacobs

Abstract

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-to-end 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 of this network is a dense estimate of the geospatial function in the form of a pixel-level labeling of the overhead image. 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. We find that our approach is more accurate for all tasks, in some cases dramatically so.

ICCV 2017 Paper

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Citation

@inproceedings{workman2017unified,
  author={Scott Workman and Menghua Zhai and David J. Crandall and Nathan Jacobs},
  title={{A Unified Model for Near and Remote Sensing}},
  booktitle={{IEEE International Conference on Computer Vision (ICCV)}},
  year=2017
}

Models & Code

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CVSoN Dataset

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