Remote Sensing is one of the relatively complex problems in Machine Learning because of spatial patterns and intricate geometric structures of the data that make semantic understanding meaning essential in the remote sensing community. CNN is one of the Machine Learning methods often used in Remote Sensing problems. However, high-resolution aerial view classification often leverages large-scale data with a huge number of parameters of the CNN model. That large number of parameters makes it hard to be applied to remote imaging peripherals because it requires high capacity of storage and memory. We propose a training framework to solve that problem that produces a model with minimum parameters without sacrificing accuracy. In this study, the three CNN architectures are used to be compared: ResNet, Inception, and EfficientNet. Furthermore, the most efficient CNN backbone then leverages Freezing layers, adding Weighted Loss and Sparse Regularization during training and then Pruning after training. Using this method on the AID Dataset, the best results are achieved by EfficientNet-B0 with Freeze 2 Layer, INS Weighted Loss, Sparse regularization with lambda = 0.001, and Global Unstructured Conv2D Pruning with an accuracy of 95.86% on test data with total parameters of 2,463,501. This study proves that Weighted Loss and Sparse Regularization can help the model to improve accuracy while Pruning enhances efficiency by cutting the model parameters into a few.