A Framework for Collecting and Classifying Objects in Satellite Imagery

  • Aswathnarayan Radhakrishnan
  • Jamie Cunningham
  • Jim DavisEmail author
  • Roman Ilin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11845)


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.


Satellite imagery Data generation Automated ground truthing OpenStreetMap Deep learning Solar power plants 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Aswathnarayan Radhakrishnan
    • 1
  • Jamie Cunningham
    • 1
  • Jim Davis
    • 1
    Email author
  • Roman Ilin
    • 2
  1. 1.Ohio State UniversityColumbusUSA
  2. 2.AFRL/RYAP, Wright-Patterson AFBDaytonUSA

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