Publication:
Identification of Residential and Commercial Area using Convolutional Neural Network

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dc.contributor.authorValliappan Raman
dc.contributor.authorPutra Sumari
dc.contributor.authorPrabhavathy M
dc.contributor.authorSundresan Perumal
dc.date.accessioned2024-10-15T08:30:38Z
dc.date.available2024-10-15T08:30:38Z
dc.date.issued2024-10-08
dc.descriptionMalaysian Journal of Science Health & Technology, Volume 10 Issue 2 Page (165–175)
dc.description.abstract<jats:p>Abstract— Image classification of land use using aerial scene classification has become increasingly common around the world. Utilizing the power of Convolutional Neural Networks (CNNs), identification of various city township areas using satellite imagery has become more efficient compared to the previous manual labeling. The objective of this research is to build a convolutional neural network model for residential and commercial area identification. In the research, we also adopted Inception V3 and VGG16 to develop two transfer learning models for the identification system. The Inception V3-based model achieved the highest overall accuracy value of 100%, showing its effectiveness in accurate residential and commercial area identification. The proposed CNN model achieved an accuracy of 99%, while the VGG-16 model with all configurations being frozen achieved 99% accuracy.</jats:p>
dc.identifier.citationValliappan Raman, Putra Sumari, Prabhavathy M, & Sundresan Perumal. (2024). Identification of Residential and Commercial Area using Convolutional Neural Network . Malaysian Journal of Science Health & Technology, 10(2), 165–175. https://doi.org/10.33102/mjosht.v10i2.396
dc.identifier.doi10.33102/mjosht.v10i2.396
dc.identifier.urihttps://mjosht.usim.edu.my/index.php/mjosht/article/view/396/237
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/23573
dc.language.isoen_US
dc.publisherUniversiti Sains Islam Malaysia
dc.relation.ispartofMalaysian Journal of Science Health &amp; Technology
dc.relation.issn2601-0003
dc.subjectCNNs
dc.subjectTransfer Learning
dc.subjectSatellite Image
dc.titleIdentification of Residential and Commercial Area using Convolutional Neural Network
dc.typetext::journal::journal article::research article
dspace.entity.typePublication
oaire.citation.endPage175
oaire.citation.issue2
oaire.citation.startPage165
oaire.citation.volume10
oairecerif.author.affiliation#PLACEHOLDER_PARENT_METADATA_VALUE#
oairecerif.author.affiliation#PLACEHOLDER_PARENT_METADATA_VALUE#
oairecerif.author.affiliation#PLACEHOLDER_PARENT_METADATA_VALUE#
oairecerif.author.affiliationUniversiti Sains Islam Malaysia

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