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

Version 4 2020-07-30, 19:03
Version 3 2018-10-02, 19:55
Version 2 2016-09-25, 21:28
Version 1 2016-05-27, 21:54
Posted on 2020-07-30 - 19:03 authored by Kyle Bradbury
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 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, and analysis of the socioeconomic correlates of PV deployment.


3 Biotech
3D Printing in Medicine
3D Research
3D-Printed Materials and Systems
AAPG Bulletin
AAPS PharmSciTech
Abhandlungen aus dem Mathematischen Seminar der Universität Hamburg
ABI Technik (German)
Academic Medicine
Academic Pediatrics
Academic Psychiatry
Academic Questions
Academy of Management Discoveries
Academy of Management Journal
Academy of Management Learning and Education
Academy of Management Perspectives
Academy of Management Proceedings
Academy of Management Review
Select your citation style and then place your mouse over the citation text to select it.


need help?