Application of Different CNN Models for Land-Use and Land-Cover Classification

A prompt change over an area can be observed in Land Use and Land Cover maps (LULC) brought by several factors, thus classifying LULC can give vital information such as the scale of commercialized and residential lands, and the availability of forest and barren land. In this study, CNN using pre-trained models such as ResNet-50, VGG16, and EfficientNetB0 was applied to LULC dataset to classify the satellite images into seven classes. The pre-trained model that exhibits a highest performance is the ResNet50 with an Intersection over Union (IoU) of 0.67499