Duke University Energy Initiative
L o a d i n g
The Duke University Energy Initiative is a university-wide interdisciplinary collaboration focused on advancing an accessible, affordable, reliable, and clean energy system. The Initiative reaches across business, engineering, environment, law, policy, and the arts and sciences to educate tomorrow’s energy innovators, develop new solutions through research, and improve energy decisions by engaging business and government leaders.
Available DatasetsShowing 2 of 2 results
- 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.1Licence not specifiedover 1 year ago
- This dataset contains remote sensing data for every village in the state of Bihar, India. For most of these villages, the data contains the corresponding electrification rate as reported by the Garv data platform from the Indian government as of July 2017. This dataset contains satellite imagery, political boundaries, lights at night imagery, rainfall measurements, and vegetation indices data for 45,220 villages and the electrification rate data for 32,817 of those villages. This dataset may be of particular interest to those investigating how electricity access maps to infrastructure and agricultural production. This dataset was compiled as part of the Summer 2017 Duke University Data+ team, titled "Electricity Access in Developing Countries from Aerial Imagery."1Licence not specifiedover 1 year ago
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