  High quality energy systems information is a crucial input to energy systems
research, modeling, and decision-making. Unfortunately, actionable information
about energy systems is often of limited availability, 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, the use of these data is frequently challenged by its sheer volume and
complexity, precluding 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. Here we present a systematic review of the literature on this
emerging topic, providing an in-depth survey and review 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 and trends in the literature as a whole, and discuss opportunities
for innovation. These include the opportunity to extend the methods beyond
electricity to broader energy systems and wider geographic areas; and the
ability to expand the use of these methods in research and decision making as
satellite data become cheaper and easier to access. We also find that there are
persistent challenges: limited standardization and rigor of performance
assessments; limited sharing of code, which would improve replicability; and a
limited consideration of the ethics and privacy of data.
