This research paper proposes a methodology for classifying aerial photographs and lands using deep learning with transfer learning. The study utilizes the Aerial Image Dataset (AID), which contains a diverse set of aerial images with 30 scene classes. The proposed methodology involves data preprocessing, dataset splitting, training images, model selection, model training, and evaluation using performance measures. Three neural network models (ResNet50, VGG19, and EfficientNetB3) are compared, and the best model is selected based on performance metrics such as precision, recall, F1-score, and the confusion matrix. The results show the effectiveness of the proposed methodology in accurately classifying aerial photographs. This indicates that EfficientNetB3 has a higher ability to classify aerial photographs and lands compared to ResNet50 and VGG19. ResNet50 achieved moderate performance with relatively lower precision, recall, and F1-score compared to EfficientNetB3. VGG19, on the other hand, demonstrated the lowest performance across all metrics, showing low precision, recall, and F1-score values. These results can contribute to various applications such as urban planning, real estate development, and land management.