The Power of Progressive Active Learning in Floorplan Images for Energy Assessment

Floorplan energy assessments present a highly efficient method for evaluating the energy efficiency of residential properties without requiring physical presence. By employing computer modeling, accurate determination of the building’s heat loss or gain can be achieved, enabling planners and homeowners to devise energy-efficient renovation or redevelopment plans. However, the creation of an AI model for floorplan element detection necessitates the manual annotation of a substantial collection of floorplans, which poses a daunting task. This paper introduces a novel active learning model designed to detect and annotate the primary elements within floorplan images, aiming to assist energy assessors in automating the analysis of such images—an inherently challenging problem due to the time-intensive nature of the annotation process. Our active learning approach initially trained on a set of 500 annotated images and progressively learned from a larger dataset comprising 4,500 unlabeled images. This iterative process resulted in an mean average precision score of 0.833, precision score of 0.972, and recall score of 0.950. The annotated dataset will be made publicly available under a Creative Commons license.