Classification of Aerial Images and Applications: A Deep Learning Perspective

Aerial image detection is a crucial technology for a variety of applications, including but not limited to urban planning, environmental monitoring, and disaster management. In this project, we implement deep learning algorithms, in particular the AlexNet model to classify aerial images taken from different landscapes. The dataset includes cityscape, road, and nature images, which are resized and normalized to make sure these image inputs conform to the input size for the AlexNet model. Fine-tuning means changing the top layer of AlexNet to have the corresponding number of classes as that of the dataset. Using pre-trained weights helps speed up the time it will take to learn the model set up in pytorch. We use momentum Stochastic Gradient Descent (SGD) to optimize the model, and a learning rate scheduler to boost the performance. The data is then divided into training and testing sets, and the accuracy of the model is assessed using the test set. The classification results indicate that fine-tuned alexnet model work well in different aerial image classification. We present the full potential of deep learning for fast aerial image classifications, which will serve as an accurate tool for aerial image classification with applications in disaster response, urban development and environmental monitoring. This model can be further improved for different datasets and for real time image processing problems.