Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis

High quality energy systems information is a crucial input to energy systems research, modeling, and decision-making. Unfortunately, precise information about energy systems is often of limited availability and resolution, incomplete, or only accessible for a substantial fee or through a non-disclosure agreement. Recently, remotely sensed data (e.g., satellite imagery, aerial photography) have emerged as a potentially rich source of energy systems information. However, use of these data is frequently challenged by the sheer volume of unstructured information within imagery that precludes manual analysis. Recent breakthroughs in machine learning have enabled automated and rapid extraction of useful information from remotely sensed data, facilitating large-scale acquisition of critical energy system variables. Collectively, these techniques represent a powerful emerging tool to supply data to downstream modeling and decision-making for energy researchers. Here we present a systematic review of the literature on this emerging topic, providing an in-depth survey of papers published within the past two decades. We first taxonomize the existing literature into ten major areas, spanning the energy value chain. Within each research area, we distill and critically discuss major features that are relevant to energy researchers, including for example, key challenges regarding the accessibility and reliability of the methods. We then synthesize our findings to identify limitations or trends in the research as a whole, and discuss opportunities for programmatic or technical innovations.

Recommended citation: Ren, S., Hu, W., Bradbury, K., Harrison-Atlas,D., Valeri L.M., Murray, B., and Malof J., (2021). Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis