A Comprehensive Review of Deep Learning Techniques for Remote Sensing Image Analysis: Methods, Applications, Challenges, and Future Directions
Keywords:
Remote Sensing, Deep Learning, Semantic Segmentation, Vision Transformers, Image Classification, Convolutional Neural Networks, Explainable AIAbstract
Deep learning has rapidly advanced remote sensing image analysis, However, existing review papers typically focus on a single type of deep learning architecture or lack a structured comparative evaluation across tasks. Therefore, this paper provides a structured and critical review of deep learning for remote sensing image analysis on five major model families— Convolutional Neural Networks (CNNs), Transformer-based architectures, Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and hybrid CNN-Transformer models — and evaluates their performance across four core remote sensing tasks: semantic segmentation, object detection, image classification, and change detection. Our work provides a unified comparative framework that highlights architectural trade-offs, dataset-specific performance trends, and task suitability — a perspective not provided by prior surveys. We also highlight several key challenges (e.g., data scarcity, domain shift, class imbalance, and computational complexity) alongside recent solutions (e.g., self-supervised learning, multimodal fusion, lightweight networks, and explainable AI). We hope this review can provide a structured roadmap for developing scalable, efficient, and interpretable remote sensing systems.References
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Copyright (c) 2026 Ledya Safwat, Mohamed Abd Elaziz (Author)
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