Employing Neural Networks Algorithm for LULC Mapping

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dc.authoridK. M. Abujayyab, Sohaib/0000-0002-6692-3567
dc.contributor.authorAbujayyab, Sohaib K. M.
dc.contributor.authorKaras, Ismail Rakip
dc.date.accessioned2024-09-29T16:06:43Z
dc.date.available2024-09-29T16:06:43Z
dc.date.issued2020
dc.departmentKarabük Üniversitesien_US
dc.description1st International Conference on Applied Geoinformatics (ISAG) -- NOV 07-09, 2019 -- Istanbul, TURKEYen_US
dc.description.abstractLand use/land cover (LULC) maps represent a primary requirement for several geospatial applications around the world such as change detection, time series analysis, environment, and urban researches. Mapping LULC from remotely sensed data based on satellite image classification handle the rapid changes in extensive geographical areas. Several effective and efficient mechanisms suggested for supervised satellite image classification. The neural networks machine learning algorithm became a major method in supervised satellite image classification. The objective of this article is to employ neural networks as a machine learning algorithm for LULC mapping. The study applied in Ankara area, which is the capital city of Turkey. This work utilized a free Landsat 8 satellite image with the Operational Land Imager OLI sensor to implement the analysis. The image was obtained and processed in ArcGIS software. Then, the machine learning data set developed using Python scripting language. Every band out of 8 bands from Landsat 8 image considered as an explanatory variable, while the output variable defined based on visual interpretation. The training dataset built based on the signature file and random sample points. The training dataset divided into three sections, for training, for validation and the last section for testing. The training and testing processes were implemented using Google-Tensor Flow Keres library from Anaconda distribution. Feedforward neural network structure implemented with 500 neurons in the hidden layer. Confusion matrix used as accuracy assessment metrics to measure the performance of the developed model. The overall accuracy of the developed model was 92%. In terms of overall accuracy and robustness, the neural networks algorithm was effectively implemented and the LULC map produces. The model gained high accuracy that it is satisfied with the geospatial accuracy target. The consequence showed the competence of neural networks algorithm to generating LULC maps from Landsat 8 satellite images.en_US
dc.description.sponsorshipTUBITAK (The Scientific and Technological Research Council of Turkey) [2221]en_US
dc.description.sponsorshipThis study has been supported by 2221 - Fellowship Program of TUBITAK (The Scientific and Technological Research Council of Turkey). We are indebted for their supports.en_US
dc.identifier.doi10.22364/bjmc.2020.8.2.12
dc.identifier.endpage378en_US
dc.identifier.issn2255-8942
dc.identifier.issn2255-8950
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85091162847en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage370en_US
dc.identifier.urihttps://doi.org/10.22364/bjmc.2020.8.2.12
dc.identifier.urihttps://hdl.handle.net/20.500.14619/7007
dc.identifier.volume8en_US
dc.identifier.wosWOS:000543336000013en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherUniv Latviaen_US
dc.relation.ispartofBaltic Journal of Modern Computingen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMachine Learningen_US
dc.subjectNeural Networksen_US
dc.subjectLand Use/Land Cover (LULC)en_US
dc.subjectSatellite Image Classificationen_US
dc.titleEmploying Neural Networks Algorithm for LULC Mappingen_US
dc.typeConference Objecten_US

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