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

Al-Turki, Dhoyazan and Kyriakou, Marios and Basurra, Shadi and Gaber, Mohamed Medhat and Abdelsamea, Mohammed M. (2023) The power of progressive active learning in floorplan images for energy assessment. Scientific Reports, 13 (1). ISSN 2045-2322

s41598-023-42276-x.pdf - Published Version
Available under License Creative Commons Attribution.

Download (3MB)


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.

Item Type: Article
Identification Number:
7 September 2023Accepted
27 September 2023Published Online
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Gemma Tonks
Date Deposited: 17 Oct 2023 12:40
Last Modified: 17 Oct 2023 12:40

Actions (login required)

View Item View Item


In this section...