A Unified Model for Near and Remote Sensing

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

41 Scopus citations


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 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.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017
Number of pages10
ISBN (Electronic)9781538610329
StatePublished - Dec 22 2017
Event16th IEEE International Conference on Computer Vision, ICCV 2017 - Venice, Italy
Duration: Oct 22 2017Oct 29 2017

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
ISSN (Print)1550-5499


Conference16th IEEE International Conference on Computer Vision, ICCV 2017

Bibliographical note

Funding Information:
We gratefully acknowledge the support of NSF CAREER grants IIS-1553116 (Jacobs) and IIS-1253549 (Crandall), a Google Faculty Research Award (Jacobs), and an equipment donation from IBM to the University of Kentucky Center for Computational Sciences.

Publisher Copyright:
© 2017 IEEE.

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition


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