Land Cover and Land Use Extraction Based on Deep Learning Methods Using Satellite Images

Information on land use and cover needs to be gathered due to the growing urban population, city growth, and urbanization.Applications for this data include environmental protection, urban planning, planning for urban infrastructure, and strategic planning to guarantee the sustainable growth of urban areas.The primary source of data on land cover and land use at the moment is remote sensing imagery.Information about land cover and land use can be retrieved from remote sensing images using image classification techniques.In terms of classification accuracy, deep learning techniques recently outperformed other methods for classifying land use and cover.Convolutional neural networks (CNNs), which are quite popular in this field, are one of the significant deep learning classification architectures frequently used in land cover and land use classification.Recently, the convolutional neural network technique known as ResNet has been applied to remote sensing applications, particularly for the classification of land use and cover.ResNet models are an effective choice for classifying land cover and land use because they can handle the vanishing gradient issue.The primary objective of this study is to assess the performance of the Glorot Uniform and Random Uniform weight initializers in the ResNet50, ResNet101, and ResNet152 architectures for extracting the land cover and land use of the EuroSat dataset.The weighted F1 score, IoU indexes, overall accuracy, and kappa coefficient were used to evaluate the accuracy of the results.ResNet101's corresponding values for these indexes were, in turn, 0.8869, 0.7951, 0.8871, and 0.8743.These results indicate that, in terms of classification accuracy, ResNet101 has outperformed the ResNet50 and ResNet152 methods.