TY  - JOUR
AU  - Samardžić-Petrović, Mileva
AU  - Dragićević, Suzana
AU  - Kovačević, Miloš
AU  - Bajat, Branislav
PY  - 2016
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/777
AB  - Support Vector Machines (SVM) is a machine learning (ML) algorithm commonly applied to the classification of remotely sensing data and more recently for modeling land use changes. However, in most geospatial applications the current literature does not elaborate on specifications of the SVM method with respect to data sampling, attribute selection and optimal parameters choices. Therefore the main objective of this study is to present and investigate the SVM technique for modeling urban land use change. The SVM model building procedure is presented together with the detailed evaluation of the output results with respect to the choice of datasets, attributes and the change of SVM parameters. Geospatial datasets containing nine land use classes and spatial attributes for the Municipality of Zemun, Republic of Serbia were used for years 2001, 2003, 2007 and 2011. The Correlation-based Feature Subset method, kappa coefficient, Area Under Receiver Operating Characteristic Curve (AUC) and kappa simulation were used to perform the model evaluation and compare the model outputs with the real land use datasets. The obtained results indicate that the SVM-based models perform better when implementing balanced data sampling, reduced data sets to informative subsets of attributes and properly identify the optimal learning parameters.
PB  - Blackwell Publishing Ltd
T2  - Transactions in Gis
T1  - Modeling Urban Land Use Changes Using Support Vector Machines
EP  - 734
IS  - 5
SP  - 718
VL  - 20
DO  - 10.1111/tgis.12174
ER  - 

@article{
author = "Samardžić-Petrović, Mileva and Dragićević, Suzana and Kovačević, Miloš and Bajat, Branislav",
year = "2016",
abstract = "Support Vector Machines (SVM) is a machine learning (ML) algorithm commonly applied to the classification of remotely sensing data and more recently for modeling land use changes. However, in most geospatial applications the current literature does not elaborate on specifications of the SVM method with respect to data sampling, attribute selection and optimal parameters choices. Therefore the main objective of this study is to present and investigate the SVM technique for modeling urban land use change. The SVM model building procedure is presented together with the detailed evaluation of the output results with respect to the choice of datasets, attributes and the change of SVM parameters. Geospatial datasets containing nine land use classes and spatial attributes for the Municipality of Zemun, Republic of Serbia were used for years 2001, 2003, 2007 and 2011. The Correlation-based Feature Subset method, kappa coefficient, Area Under Receiver Operating Characteristic Curve (AUC) and kappa simulation were used to perform the model evaluation and compare the model outputs with the real land use datasets. The obtained results indicate that the SVM-based models perform better when implementing balanced data sampling, reduced data sets to informative subsets of attributes and properly identify the optimal learning parameters.",
publisher = "Blackwell Publishing Ltd",
journal = "Transactions in Gis",
title = "Modeling Urban Land Use Changes Using Support Vector Machines",
pages = "734-718",
number = "5",
volume = "20",
doi = "10.1111/tgis.12174"
}

Samardžić-Petrović, M., Dragićević, S., Kovačević, M.,& Bajat, B.. (2016). Modeling Urban Land Use Changes Using Support Vector Machines. in Transactions in Gis
Blackwell Publishing Ltd., 20(5), 718-734.
https://doi.org/10.1111/tgis.12174

Samardžić-Petrović M, Dragićević S, Kovačević M, Bajat B. Modeling Urban Land Use Changes Using Support Vector Machines. in Transactions in Gis. 2016;20(5):718-734.
doi:10.1111/tgis.12174 .

Samardžić-Petrović, Mileva, Dragićević, Suzana, Kovačević, Miloš, Bajat, Branislav, "Modeling Urban Land Use Changes Using Support Vector Machines" in Transactions in Gis, 20, no. 5 (2016):718-734,
https://doi.org/10.1111/tgis.12174 . .