In this study, we developed a vegetation classification method based on satellite remote sensing, topographical information, and machine learning to reduce the cost and improve the accuracy of vegetation mapping. First, satellite images were processed by object-based classification. Then spectral information (8 spectral bands) from the satellite imagery, the vegetation index, and topographical information were added to each object. Vegetation classification models were created by one of the AI technologies, machine learning algorithms (Random Forests, Support Vector Machine), and vegetation types were classified. As a result, it was confirmed that the concordance rate between the AI classification and the field survey data was higher with 8-band spectral data, and with topographical information.
