GEOSPATIAL MACHINE LEARNING DATASETS STRUCTURING and CLASSIFICATION TOOL: CASE STUDY for MAPPING LULC from RASAT SATELLITE IMAGES

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dc.contributor.authorAbujayyab, S.K.M.
dc.contributor.authorKaras, I.R.
dc.date.accessioned2024-09-29T16:16:05Z
dc.date.available2024-09-29T16:16:05Z
dc.date.issued2019
dc.departmentKarabük Üniversitesien_US
dc.description6th International Conference on Geomatics and Geospatial Technology, GGT 2019 -- 1 October 2019 through 3 October 2019 -- Kuala Lumpur -- 158822en_US
dc.description.abstractRemote sensing satellite images plays a significant role in mapping land use/land cover LULC. Machine learning ML provide robust functions for satellite image classification. The objective of this paper is to extend the capability of GIS specialists in geospatial area with minimum knowledge in computer science to easily perform ML satellite image classification. A framework consisting 7 stages established. Tools of steps developed in two programing environments, which are ArcGIS for geospatial datasets structuring and Anaconda for ML training and classification. During the development, authors constrained to reduce the complexity of big data of satellite images and limited memory of computers to make tools available for implementation in PC. In addition, automation and improving the performance accuracy. TensorFlow-Keras library employed to perform the classification using neural networks. A case study using RASAT satellite image in Ankara-Turkey utilized to perform the analysis. The developed classifier gained 80% performance accuracy. The complete RASAT satellite image processed and smoothly classified based on blocks methods. The developed tools successfully tested and applied in geospatial area and can be effectively execute in PC by GIS specialist. © 2019 S. K. M. Abujayyab.en_US
dc.description.sponsorshipTurkish State Planning Office; TUBITAK UZAY; DPT; TUBITAK Space Technologies Research Institute; TUBITAK; Türkiye Bilimsel ve Teknolojik Araştirma Kurumu, TÜBITAKen_US
dc.identifier.doi10.5194/isprs-archives-XLII-4-W16-39-2019
dc.identifier.endpage46en_US
dc.identifier.issn1682-1750
dc.identifier.issue4/W16en_US
dc.identifier.scopus2-s2.0-85083154302en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage39en_US
dc.identifier.urihttps://doi.org/10.5194/isprs-archives-XLII-4-W16-39-2019
dc.identifier.urihttps://hdl.handle.net/20.500.14619/8853
dc.identifier.volume42en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInternational Society for Photogrammetry and Remote Sensingen_US
dc.relation.ispartofInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archivesen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectdatasets structuringen_US
dc.subjectlulc mappingen_US
dc.subjectmachine learningen_US
dc.subjectneural networksen_US
dc.subjectrasat satellite imagesen_US
dc.titleGEOSPATIAL MACHINE LEARNING DATASETS STRUCTURING and CLASSIFICATION TOOL: CASE STUDY for MAPPING LULC from RASAT SATELLITE IMAGESen_US
dc.typeConference Objecten_US

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