A Survey of Deep Learning for Remote Sensing, Earth Intelligence and Decision Making

This article presents a comprehensive overview of the applications of deep learning techniques in remote sensing. It discusses various neural network architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and graph neural networks, and their utilization in tasks such as land cover mapping, object detection, image segmentation, time series analysis, and change detection. Specifically focusing on Ukraine, the article highlights successful applications of deep learning, such as multi-temporal crop classification using Sentinel data, object detection for disaster monitoring, agricultural yield forecasting through RNNs and GANs, and the potential of graph neural networks in various remote sensing applications. Future prospects include further innovation in deep learning architectures, particularly transformers, and the adoption of hybrid models for Earth Intelligence and decision-making support.