Distributed Solar Photovoltaic Array Location and Extent Data Set for Remote Sensing Object Identification

Dataset

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 our environment, anthropogenic systems, and natural resources. The components of energy systems that are visible from above may be assessed with these remote sensing data when combined with machine learning methods. Here we focus on the information gap in distributed solar photovoltaic (PV) arrays, of which there is limited data on solar PV deployments at small geographic scales. We created a machine learning dataset to develop the process of automatically identifying solar PV locations through the use of remote sensing imagery.This dataset contains the geospatial coordinates and border vertices for 19,433 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, and analysis of the socioeconomic correlates of PV deployment.Links to the aerial photographs from Fresno, Stockton, Oxnard, and Modesto can be found in the references.Notes: this version of the dataset has been improved to increase the accuracy of polygon georeferencing so that the data can be more easily integrated with imagery other than than the original imagery from which the annotations were based. Additionally, a small number of polygons were found to be erroneous annotations and either corrected or removed.Update July 30, 2020: Panel area in pixels and area in square meters were labeled incorrectly in the original version; those labels were reversed. Updated files include: 'polygonDataExceptVertices.csv', 'SolarArrayPolygons.geojson', 'SolarArrayPolygons.json'.

Data Access

Duke Authors

Cited Authors

  • Bradbury, K; Saboo, R; Malof, J; Johnson, T; Devarajan, A; Zhang, W; Collins, L; Newell, R; Streltsov, A; Hu, W

Published Date

  • July 30, 2020

Digital Object Identifier (DOI)

  • 10.6084/m9.figshare.3385780