This presentation, “Mapping Energy Infrastructure with Unmanned Aerial Vehicles (UAVs) and Deep Learning,” was created by Simiao (Ben) Ren (Ph.D. student in electrical and computer engineering, Duke University) as part of the Duke University Energy Data Analytics Ph.D. Student Fellowship Program.

Abstract: “As energy systems undergo a dramatic transition to more renewable and distributed energy generation, energy security in the forthcoming decades will depend heavily upon increasingly sophisticated energy systems modeling and effective decision-making. Success in these endeavors depends crucially upon access to high-quality and detailed information about existing energy infrastructure: e.g., residential solar systems and their capacity, transmission/distribution lines, and more. Unfortunately, however, such information is often limited, incomplete, or only accessible for a substantial fee. In this work, I propose to overcome this obstacle to energy decision-making by leveraging recent breakthroughs in machine learning to develop algorithms that can automatically extract energy information from high-resolution imagery from unmanned aerial vehicles (UAVs). In recent years such algorithms have been successfully demonstrated for collecting energy information on satellite imagery, however, many types of energy infrastructure cannot be reliably identified or characterized using satellite imagery due to its limited resolution. In this work, I propose to collect UAV imagery of several types of energy infrastructure and demonstrate that these objects can be identified and characterized with unprecedented accuracy. This technique, if effective, will provide a powerful new tool that will facilitate energy modeling and decision-making, and help ensure energy security in the forthcoming decades.”

Support for this work was provided by the Alfred P. Sloan Foundation Grant G-2020-13922 through the Duke University Energy Data Analytics Ph.D. Student Fellowship.

Related publication in ISPRS International Journal of Geo-Information: https://doi.org/10.3390/ijgi11040222

Note: Conclusions reached or positions taken by researchers or other grantees represent the views of the grantees themselves and not those of the Alfred P. Sloan Foundation or its trustees, officers, or staff.

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