Advancements and challenges of deep learning architectures for aerial image analysis: A systematic review

Abstract

The rapid advancement of deep learning (DL) technologies has significantly transformed the domain of aerial image analysis. This systematic review explores the forefront of deep learning architectures specifically designed for the processing and analysis of aerial imagery. It offers a comprehensive examination of updated models, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and Transformers, highlighting their unique contributions and comparative effectiveness in aerial image analysis. This review critically compares these architectures through an extensive literature survey, focusing on their impact on enhancing accuracy, computational efficiency, and overall performance in critical aerial imaging tasks, such as classification, object detection, and semantic segmentation. Additionally, it sheds light on the innovative architectural improvements that have been crucial in overcoming traditional challenges associated with aerial image processing, such as handling high-resolution data, managing diverse and changing landscapes, and ensuring real-time analysis capabilities. By synthesizing current findings and identifying prevailing trends, this review not only charts the progress in the field but also outlines future research directions, emphasizing the need for more adaptable, robust, and efficient deep-learning solutions to meet the growing demands of aerial image analysis.