Building air leakage and moisture issues can result in significant energy loss, shorten the building envelope life cycle, and require additional maintenance costs. While these issues present risks to a building and its occupants, they could be difficult to detect during building maintenance and early stages of retrofit projects. Currently, mapping air leakage and moisture issues over a building’s envelope relies on manual inspections. These methods are intrusive, expensive, and hazardous to inspectors’ safety. To address these challenges, we propose a non-invasive and safe conceptual solution, a Robotic Envelope Assessment System for Energy Efficiency (EASEEbot), to locate and document moisture intrusion, thermal bridges, and air leaks. EASEEbot is a high-power wall climbing drone and comes with a multi-function toolbox. It can capture 3D thermal images and auto-generate 3D models using image-based Structure from Motion (SfM) and Visual Simultaneous Localization and Mapping (VSLAM). Our deep learning algorithms will rapidly identify common building envelope defects from multi-modal sensing data. EASEEbot is also designed with a tethered wall-climbing mode and will use long-wave ground penetrating radar (GPR) to detect hidden trapped interstitial moisture and other major envelope defects.