The Geo-Scientific Institute Potsdam, Carbon Delta and Climate-KIC are proud to present this paper on using remote sensing and machine learning technologies to secure higher quality asset-level data and associated GHG emissions in order to more accurately project climate-related risks & opportunities.
The exponential increase in space-based sensing, computing power, and algorithmic complexity means that the development of a global catalogue of every physical asset in the world is within technical feasibility. Accurate asset-level data can dramatically enhance the ability of investors, regulators, governments, and civil society to measure and manage different forms of environmental risk, opportunity, and impact. In particular, remote sensing can help identify the features and use of assets relevant to determining asset-level GHG emissions. This report examines the potential role of new technologies to secure better asset-level data and at higher refresh rates.