从遥感数据中自动提取能源系统信息：综述与分析
Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis
摘要：
高质量的能源系统信息是能源系统研究、建模和决策的重要输入。不幸的是，有关能源系统的可操作信息通常是有限的可用性，不完整，或只有通过大量的费用或通过保密协议才能获得。最近，遥感数据（例如，卫星图像、航空摄影）已成为能源系统信息的一个潜在的丰富来源。然而，这些数据的使用经常受到其庞大数量和复杂性的挑战，无法进行人工分析。机器学习领域最近的突破使得能够从遥感数据中自动和快速地提取有用信息，从而促进大规模获取关键的能源系统变量。在这里，我们提出了一个系统的文献综述这一新兴的话题，提供了一个深入的调查和回顾过去二十年内发表的论文。我们首先将现有的文献分类为十大领域，跨越能源价值链。在每个研究领域内，我们提取并批判性地讨论与能源研究人员相关的主要特征，包括例如有关方法的可访问性和可靠性的关键挑战。然后，我们综合我们的研究结果，以确定文献作为一个整体的局限性和趋势，并讨论创新的机会。其中包括将这些方法从电力扩展到更广泛的能源系统和更广泛的地理区域的机会;以及随着卫星数据变得更便宜和更容易获得，在研究和决策中扩大使用这些方法的能力。我们还发现，持续存在的挑战包括：业绩评估的标准化和严格程度有限;代码共享有限，而这将提高可复制性;对数据的道德和隐私的考虑有限。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
