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A Framework for Collecting and Classifying Objects in Satellite Imagery

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Advances in Visual Computing (ISVC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11845))

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Abstract

A major issue with data-hungry deep learning algorithms is the lack of annotated ground truth for specific applications. The high volume of satellite imagery available today, coupled with crowd-sourced map data can enable a new means for training and classifying objects in wide-area imagery. In this work, we present an automated pipeline for collecting and labeling satellite imagery to facilitate building custom deep learning models. We demonstrate this approach by automatically collecting labeled imagery of solar power plants and building a classifier to detect the structures. This framework can be used to collect labeled satellite imagery of any object mapped by spatial databases.

This work was supported by the U.S. Air Force Research Laboratory under contract #GRT00044839/60056946.

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Author information

Authors and Affiliations

  1. Ohio State University, Columbus, OH, 43210, USA

    Aswathnarayan Radhakrishnan, Jamie Cunningham & Jim Davis

  2. AFRL/RYAP, Wright-Patterson AFB, Dayton, OH, 45433, USA

    Roman Ilin

Authors
  1. Aswathnarayan Radhakrishnan
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  2. Jamie Cunningham
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  3. Jim Davis
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  4. Roman Ilin
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Corresponding author

Correspondence to Jim Davis .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, NV, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. University of Nevada, Reno, NV, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Daniela Ushizima

  6. Latent AI, Palo Alto, CA, USA

    Sek Chai

  7. Texas A&M University, College Station, TX, USA

    Shinjiro Sueda

  8. Louisiana State University, Baton Rouge, LA, USA

    Xin Lin

  9. University of North Carolina at Charlotte, Charlotte, NC, USA

    Aidong Lu

  10. École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Daniel Thalmann

  11. Notre Dame University, Notre Dame, IN, USA

    Chaoli Wang

  12. Bosch Research North America, Palo Alto, CA, USA

    Panpan Xu

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Radhakrishnan, A., Cunningham, J., Davis, J., Ilin, R. (2019). A Framework for Collecting and Classifying Objects in Satellite Imagery. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2019. Lecture Notes in Computer Science(), vol 11845. Springer, Cham. https://doi.org/10.1007/978-3-030-33723-0_24

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  • DOI: https://doi.org/10.1007/978-3-030-33723-0_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33722-3

  • Online ISBN: 978-3-030-33723-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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