Improving Dryland Urban Land Cover Classification Accuracy Using a Classical Convolution Neural Network

Reliable information of land cover dynamics in dryland cities is crucial for understanding the anthropogenic impacts on fragile environments. However, reduced classification accuracy of dryland cities often occurs in global land cover data. Although many advanced classification techniques (i.e., convolutional neural networks (CNN)) have been intensively applied to classify urban land cover because of their excellent performance, specific classification models focusing on typical dryland cities are still scarce. This is mainly attributed to the similar features between urban and non-urban areas, as well as the insufficient training samples in this specific region. To fill this gap, this study trained a CNN model to improve the urban land classification accuracy for seven dryland cities based on rigorous training sample selection. The assessment showed that our proposed model performed with higher overall accuracy (92.63%) than several emerging land cover products, including Esri 2020 Land Cover (75.55%), GlobeLand30 (73.24%), GLC_FCS30-2020 (69.68%), ESA WorldCover2020 (64.38%), and FROM-GLC 2017v1 (61.13%). In addition, the classification accuracy of the dominant land types in the CNN-classified data exceeded the selected products. This encouraging finding demonstrates that our proposed architecture is a promising solution for improving dryland urban land classification accuracy and compensating the deficiency of large-scale land cover mapping.

Automated summary

To improve the classification accuracy of urban land in dryland cities, this study trained a convolutional neural network (CNN) model for seven dryland cities based on rigorous training sample selection. The assessment showed that our proposed model achieved higher overall accuracy (92.63%) than several emerging land cover products. Furthermore, the classification accuracy of the dominant land types in the CNN-classified data exceeded the selected products. This finding demonstrates the promising potential of our proposed architecture in improving dryland urban land classification accuracy and compensating for the deficiency of large-scale land cover mapping.