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The power of progressive active learning in floorplan images for energy assessment. | LitMetric

AI Article Synopsis

  • Floorplan energy assessments efficiently evaluate residential energy efficiency through computer modeling, eliminating the need for physical inspections while enabling effective planning for energy-saving renovations.
  • Developing an AI model for detecting floorplan elements requires extensive manual annotation, which the authors address with a new active learning model that automates this process.
  • The active learning model was trained on 500 annotated images and expanded with 4500 unlabelled images, achieving high precision and recall scores, and the dataset is made publicly accessible under a Creative Commons license.

Article Abstract

Floorplan energy assessments present a highly efficient method for evaluating the energy efficiency of residential properties without requiring physical presence. By employing computer modelling, an 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 4500 unlabelled images. This iterative process resulted in mean average precision score of 0.833, precision score of 0.972, and recall score of 0.950. We make our dataset publicly available under a Creative Commons license.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10533859PMC
http://dx.doi.org/10.1038/s41598-023-42276-xDOI Listing

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