With the explosion of data and information, data analysis techniques are becoming increasingly important. Remote sensing image data has many advantages in analyzing features of the earth's surface due to its wide coverage and frequent updating. However, with the strong development of remote sensing technology, the source of remote sensing image data has increased rapidly. This requires powerful tools to solve problems on this data source. In recent years, artificial intelligence is developing rapidly and is one of the extremely powerful data analysis tools in many fields, including remote sensing image data. In this article, we present a solution to apply the convolutional neural network (CNN) to classify landcovers from remote sensing image data. The landcover classification experiment shows that the application of the convolutional neural network model can give classification results with an accuracy of over 95%. Meanwhile, the RF model only gives results with less than 90% accuracy. This result shows great potential for the application of deep learning models in remote sensing image analysis
