Thematic mapping of urban areas from WorldView-2 satellite imagery using machine learning algorithms

Monitoring and mapping urban areas from remote sensing imagery using image classification techniques are important for urban and regional planners and municipalities.High resolution satellite images are essential data sources for the generation of urban land-use/land-cover maps.In this paper, urban thematic maps are generated from WorldView-2 satellite images using machine learning algorithms, namely random forest and support vector machines classifiers.The performances of these classifiers are evaluated and compared using four test areas that have different urban characteristics.The obtained visual and quantitative results for four test areas indicate the effectiveness of the machine learning algorithms in urban thematic mapping.The overall accuracies were computed in the range of 89.92 and 96.38 and overall kappa values were computed in the range of 0.8790 and 0.9566 for random forest classifier, which are considerably high.Similarly, for support vector machines classifier, the overall accuracies were computed in the range of 91.98 and 96.07 and overall kappa values were computed in the range of 0.9038 and 0.9528.The results also show that different classification accuracies for different test areas are related to the properties of the selected urban patterns.