Automatic floor plan analysis and recognition

dc.coverageDOI: 10.1016/j.autcon.2022.104348
dc.creatorPizarro, Pablo N.
dc.creatorHitschfeld, Nancy
dc.creatorSipiran, Ivan
dc.creatorSaavedra, Jose M.
dc.date2022
dc.date.accessioned2025-11-18T19:43:06Z
dc.date.available2025-11-18T19:43:06Z
dc.description<p>Due to recent advances in machine learning, there has been an explosive development of multiple methodologies that automatically extract information from architectural floor plans. Nevertheless, the lack of a standard notation and the high variability in style and composition make it urgent to devise reliable and effective approaches to analyze and recognize objects like walls, doors, and rooms from rasterized images. For such reason, and with the aim of bringing some significant contribution to the state-of-the-art, this paper provides a critical revision of the methodologies and tools from rule-based and learning-based approaches between the years 1995 to 2021. Datasets, scopes, and algorithms were discussed to guide future developers to improve productivity and reduce costs in the construction and design industries. This study concludes that most research relies on a particular plan style, facing problems regarding generalization and comparison due to the lack of a standard metric and the limited public datasets. However, the study also highlights that combining existing tasks can be employed in various and increasing applications.</p>eng
dc.identifierhttps://investigadores.uandes.cl/en/publications/c3a722d5-6cd7-4370-bc36-350bbfffb551
dc.identifier.urihttps://repositorio.uandes.cl/handle/uandes/52705
dc.languageeng
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.sourcevol.140 (2022) p.1-21
dc.subjectDeep machine learning
dc.subjectFloor plan analysis
dc.subjectImage processing
dc.subjectObject detection
dc.subjectRule-based methods
dc.subjectSegmentation
dc.subjectVectorization
dc.titleAutomatic floor plan analysis and recognitioneng
dc.typeReview articleeng
dc.typeArtículo de revisiónspa
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