Land-use change dynamics and automated feature extraction using high-resolution satellite imagery

dc.contributor.authorDey, M
dc.contributor.authorPrakash, PS
dc.contributor.authorChandrashekar, CM
dc.contributor.authorAithal, BH
dc.contributor.editorPerera, N
dc.contributor.editorThibbotuwawa, A
dc.date.accessioned2022-11-05T06:05:24Z
dc.date.available2022-11-05T06:05:24Z
dc.date.issued2022-08
dc.description.abstractThe mapping of urban landscapes is a challenging task due to their dense and diverse characteristics. The changing urban environment with developing infrastructure demands constant updates and accurate extraction techniques. Recent advancement in geospatial technology has led to the capture of high-resolution data and its analysis at a finer scale. However, a sustainable development framework necessitates the understanding of spatial patterns incorporating vertical and horizontal components of the built-up volume. This study aims to understand the changing landscape at the pixel level by analysing features along with their volumetric expansion. The findings highlight that Bangalore city’s urban growth has shown increment over the period with volumetric expansion in all parts of the city based on events of changing demand and growth. The evaluation metrics indicate that the model can be generalised for any geographical location as well as to different sensor type images.en_US
dc.identifier.citation*****en_US
dc.identifier.conference7th International Conference on Research for Transport and Logistics Industry 2022en_US
dc.identifier.departmentDepartment of Transport and Logistics Managementen_US
dc.identifier.emailmadhumitadey@kgpian.iitkgp.ac.inen_US
dc.identifier.emailpsprakashgis@gmail.comen_US
dc.identifier.emailchandanmc@nie.ac.inen_US
dc.identifier.emailbharath@infra.iitkgp.ac.inen_US
dc.identifier.facultyEngineeringen_US
dc.identifier.pgnospp. 41-45en_US
dc.identifier.placeColomboen_US
dc.identifier.proceedingProceedings of 7th International Conference on Research for Transport and Logistics Industry 2022en_US
dc.identifier.urihttp://dl.lib.uom.lk/handle/123/19414
dc.identifier.year2022en_US
dc.language.isoenen_US
dc.publisherSri Lanka Society of Transport and Logisticsen_US
dc.relation.urihttps://slstl.lk/r4tli-2022/en_US
dc.subjectLand useen_US
dc.subjectUrban built-up volumeen_US
dc.subjectDeep learningen_US
dc.subjectConvolutional neural networken_US
dc.subjectFeature extractionen_US
dc.titleLand-use change dynamics and automated feature extraction using high-resolution satellite imageryen_US
dc.typeConference-Full-texten_US

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