Enhancing real estate listings through image classification and enhancement: a comparative study

Basit Öğe Kaydı
dc.authorid0000-0002-9283-5528
dc.authorid0000-0002-9698-4360
dc.authorid0000-0003-3561-9375
dc.contributor.authorKüp, Eyüp Tolunayen_US
dc.contributor.authorSözdinler, Melihen_US
dc.contributor.authorIşık, Ali Hakanen_US
dc.contributor.authorDoksanbir, Yalçınen_US
dc.contributor.authorAkpınar, Gökhanen_US
dc.date.accessioned2025-09-15T10:58:20Z
dc.date.available2025-09-15T10:58:20Z
dc.date.issued2025-05-22
dc.departmentIşık Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering and Natural Sciences, Department of Computer Engineeringen_US
dc.descriptionThe research by Emlakjet (Emlakjet \u0130nternet Hizmetleri ve Gayrimenkul Dan\u0131\u015Fmanl\u0131\u011F\u0131 Anonim \u015Eirketi) was carried out at the Emlakjet Research and Development Center with financial support from The Scientific and Technological Research Council of T\u00FCrkiye (T\u00DCB\u0130TAK) (Grant No: 7220634).en_US
dc.description.abstractWe extended real estate property listings on the online prop-tech platform. On the platform, the images were classified into the specified classes according to quality criteria. The necessary interventions were made by measuring the platform’s appropriateness level and increasing the advertisements’ visual appeal. A dataset of 3000 labeled images was utilized to compare different image classification models, including convolutional neural networks (CNNs), VGG16, residual networks (ResNets), and the LLaVA large language model (LLM). Each model’s performance and benchmark results were measured to identify the most effective method. In addition, the classification pipeline was expanded using image enhancement with contrastive unsupervised representation learning (CURL). This method assessed the impact of improved image quality on classification accuracy and the overall attractiveness of property listings. For each classification model, the performance was evaluated in binary conditions, with and without the application of CURL. The results showed that applying image enhancement with CURL enhances image quality and improves classification performance, particularly in models such as CNN and ResNet. The study results enable a better visual representation of real estate properties, resulting in higher-quality and engaging user listings. They also underscore the importance of combining advanced image processing techniques with classification models to optimize image presentation and categorization in the real estate industry. The extended platform offers information on the role of machine learning models and image enhancement methods in technology for the real estate industry. Also, an alternative solution that can be integrated into intelligent listing systems is proposed in this study to improve user experience and information accuracy. The platform proves that artificial intelligence and machine learning can be integrated for cloud-distributed services, paving the way for future innovations in the real estate sector and intelligent marketplace platforms.en_US
dc.description.sponsorshipTürkiye Bilimsel ve Teknolojik Araştırma Kurumuen_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationKüp, E. T., Sözdinler, M., Işık, A. H., Doksanbir, Y. & Akpınar, G. (2025). Enhancing real estate listings through image classification and enhancement: a comparative study. Engineering Proceedings, 92(1), 1-7. doi:https://doi.org/10.3390/engproc2025092080en_US
dc.identifier.doi10.3390/engproc2025092080
dc.identifier.endpage7
dc.identifier.issn2673-4591
dc.identifier.issue1
dc.identifier.scopus2-s2.0-105009269649
dc.identifier.scopusqualityQ3
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/11729/6702
dc.identifier.urihttps://doi.org/10.3390/engproc2025092080
dc.identifier.volume92
dc.indekslendigikaynakScopusen_US
dc.institutionauthorSözdinler, Melihen_US
dc.institutionauthorid0000-0002-9698-4360
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)en_US
dc.relation.ispartofEngineering Proceedingsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectContrastive unsupervised representation learningen_US
dc.subjectConvolutional neural networksen_US
dc.subjectImage classificationen_US
dc.subjectImage enhancementen_US
dc.subjectLarge language modelsen_US
dc.subjectProp-techen_US
dc.subjectReal estateen_US
dc.subjectResneten_US
dc.subjectRoom classificationen_US
dc.subjectVGG16en_US
dc.titleEnhancing real estate listings through image classification and enhancement: a comparative studyen_US
dc.typeArticleen_US
dspace.entity.typePublicationen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
Enhancing_real_estate_listings_through_image_classification_and_enhancement_a_comparative_study.pdf
Boyut:
397.9 KB
Biçim:
Adobe Portable Document Format
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
license.txt
Boyut:
1.17 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Makale Koleksiyonu | Bilgisayar Mühendisliği Bölümü
Scopus İndeksli Yayınlar Koleksiyonu