On measuring the change in historical city centres: an attempt at comparing human perception and deep learning through visual quality of street space

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dc.contributor.authorGönül, Alper
dc.contributor.authorDurak, Selen
dc.date.accessioned2026-02-08T15:11:05Z
dc.date.available2026-02-08T15:11:05Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractThe quality of street space serves as a pivotal factor in overseeing the preservation, development, and utilization of historic heritage sites by individuals. This study proposes a novel method for quantifying changes in historical environments by assessing visual space quality. The model integrates artificial intelligence (AI)-based image segmentation of street views, representing an indirect form of human perception, with diverse user opinions based on evocation of facade images, reflecting direct human perception. The aim of the study is to evaluate visual space quality by comparing artificial intelligence and human perception within the proposed model, thereby harnessing the strengths of both approaches. The Atatürk High Street of Bursa City, situated within the Khans region, which inscribed on the UNESCO heritage list, was utilized as the study area to validate the method. Workstations, 50 m far away each other were created on Atatürk Street, and 360-degree panoramic images were obtained from these stations with Google Street View and action camera shots for the years 2014, 2018, 2020 and 2023. The obtained images were analyzed with deep learning-based semantic segmentation technique to monitor the changes in the visual quality indicators of greenery, openness, enclosure, imageability, walkability and complexity. The facade images of the workstations were shown to the experts and stakeholders with a survey application, and subjective-semi subjective change was determined over the same parameters. In the assessment of visual space quality, indicators such as openness, greenery, and enclosure, which predominantly encompass physical components, tend to yield objective and subjective results that closely align with each other. Conversely, discrepancies between objective and subjective results emerge for indicators such as imageability and complexity, wherein human emotions and perception exert significant influence. © The Author(s) 2025.
dc.description.sponsorshipTürkiye Bilimsel ve Teknolojik Araştırma Kurumu, TUBITAK
dc.identifier.doi10.1007/s11042-025-20642-3
dc.identifier.endpage39305
dc.identifier.issn1380-7501
dc.identifier.issue32
dc.identifier.scopus2-s2.0-85217406498
dc.identifier.scopusqualityQ1
dc.identifier.startpage39283
dc.identifier.urihttps://doi.org/10.1007/s11042-025-20642-3
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5230
dc.identifier.volume84
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofMultimedia Tools and Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzScopus_KA_20260207
dc.subjectBursa
dc.subjectHuman perception
dc.subjectSemantic segmentation
dc.subjectUrban change
dc.subjectVisual quality
dc.titleOn measuring the change in historical city centres: an attempt at comparing human perception and deep learning through visual quality of street space
dc.typeArticle

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