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Remotely-sensed data for urban classification is very diverse in data type, acquisition time, and spatial resolution. Therefore, preprocessing is needed for input data, in which the spatial resolution must be changed by different resampling methods. However, data transformations during resampling have many effects on classification results. In this research, resampling methods were evaluated. The results showed that mean aggregation and bicubic interpolation methods performed better than the rest on a variety of data types. Besides, the highest overall accuracy and the F1 score for urban classification maps were 98.47% and 0.9842, respectively. DOI: 10.32913/rd-ict.vol2.no15.663

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