Deep learning has become a key tool in remote sensing, offering powerful solutions for data analysis. This review discusses the challenges and recent advances in applying deep learning to remote sensing, emphasizing its potential for tackling large-scale challenges like climate change and urbanization. The paper highlights the importance of integrating domain expertise with deep learning to improve performance and address the unique characteristics of remote sensing data, such as multi-modal and geo-located information. It also addresses the need for incorporating physical models into deep learning frameworks to enhance accuracy and reliability. The review covers various applications, including hyperspectral image analysis, SAR image interpretation, high-resolution satellite image interpretation, multimodal data fusion, and 3D reconstruction. It discusses different deep learning architectures, such as autoencoders, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), and their applications in remote sensing tasks. The paper also provides tools and resources for researchers to start using deep learning in their work, including open-source codes and data repositories. The review concludes with an overview of the challenges and future directions in deep learning for remote sensing.Deep learning has become a key tool in remote sensing, offering powerful solutions for data analysis. This review discusses the challenges and recent advances in applying deep learning to remote sensing, emphasizing its potential for tackling large-scale challenges like climate change and urbanization. The paper highlights the importance of integrating domain expertise with deep learning to improve performance and address the unique characteristics of remote sensing data, such as multi-modal and geo-located information. It also addresses the need for incorporating physical models into deep learning frameworks to enhance accuracy and reliability. The review covers various applications, including hyperspectral image analysis, SAR image interpretation, high-resolution satellite image interpretation, multimodal data fusion, and 3D reconstruction. It discusses different deep learning architectures, such as autoencoders, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), and their applications in remote sensing tasks. The paper also provides tools and resources for researchers to start using deep learning in their work, including open-source codes and data repositories. The review concludes with an overview of the challenges and future directions in deep learning for remote sensing.