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

Kyle Bradbury, Raghav Saboo, Timothy L. Johnson, Jordan M. Malof, Arjun Devarajan, Wuming Zhang, Leslie M. Collins, Richard G. Newell

Research output: Contribution to journalArticlepeer-review

78 Scopus citations


Earth-observing remote sensing data, including aerial photography and satellite imagery, offer a snapshot of the world from which we can learn about the state of natural resources and the built environment. The components of energy systems that are visible from above can be automatically assessed with these remote sensing data when processed with machine learning methods. Here, we focus on the information gap in distributed solar photovoltaic (PV) arrays, of which there is limited public data on solar PV deployments at small geographic scales. We created a dataset of solar PV arrays to initiate and develop the process of automatically identifying solar PV locations using remote sensing imagery. This dataset contains the geospatial coordinates and border vertices for over 19,000 solar panels across 601 high-resolution images from four cities in California. Dataset applications include training object detection and other machine learning algorithms that use remote sensing imagery, developing specific algorithms for predictive detection of distributed PV systems, estimating installed PV capacity, and analysis of the socioeconomic correlates of PV deployment.

Original languageEnglish
Article number160106
JournalScientific data
StatePublished - Dec 6 2016


Dive into the research topics of 'Distributed solar photovoltaic array location and extent dataset for remote sensing object identification'. Together they form a unique fingerprint.

Cite this