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Distributed solar photovoltaic array location and extent dataset for remote sensing object identification.

Kyle Bradbury1, Raghav Saboo2, Timothy L Johnson3

  • 1Energy Initiative, Duke University, Durham, North Carolina 27708, USA.

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Researchers created a new dataset of over 19,000 solar panels from remote sensing images to help identify distributed solar photovoltaic (PV) systems. This data aids in understanding solar energy growth and deployment patterns.

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Area of Science:

  • Earth Science
  • Renewable Energy
  • Computer Science

Background:

  • Earth-observing remote sensing data provides insights into natural resources and the built environment.
  • Machine learning methods can automatically assess energy systems from aerial and satellite imagery.
  • Limited public data exists on small-scale distributed solar photovoltaic (PV) deployments.

Purpose of the Study:

  • To address the information gap in distributed solar PV deployments.
  • To create a dataset for automatically identifying solar PV arrays using remote sensing imagery.
  • To facilitate machine learning applications for PV system analysis.

Main Methods:

  • Utilized high-resolution aerial and satellite imagery from four California cities.
  • Developed a process to automatically identify solar PV locations.
  • Created a dataset containing geospatial coordinates and border vertices for over 19,000 solar panels.

Main Results:

  • A comprehensive dataset of distributed solar PV arrays was successfully generated.
  • The dataset includes detailed information on over 19,000 individual solar panels.
  • The data covers 601 images across four distinct urban areas.

Conclusions:

  • The created dataset is valuable for training machine learning algorithms for PV detection.
  • Applications include estimating installed PV capacity and analyzing socioeconomic correlates of PV deployment.
  • This work advances the automated assessment of distributed solar energy systems using remote sensing.