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

By Kyle Bradbury, Raghav Saboo, Timothy L Johnson, Jordan M. Malof, Arjun Devarajan, Wuming Zhang, Leslie M Collins, Richard G Newell. Scientific Data, 2016

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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. Design Type(s) data integration objective • observation design Measurement Type(s) solar photovoltaic array location Technology Type(s) digital curation Factor Type(s) Sample Characteristic(s) City of Fresno • City of Stockton • City of Modesto • City of Oxnard Design Type(s) data integration objective • observation design Measurement Type(s) solar photovoltaic array location Technology Type(s) digital curation Factor Type(s) Sample Characteristic(s) City of Fresno • City of Stockton • City of Modesto • City of Oxnard Machine-accessible metadata file describing the reported data (ISA-Tab format)

Subfields: Environmental Engineering, Media Technology, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/a9e26decac48eca32ec2e05dbdf66ccce0263120