This article reviews the application of deep learning in remote sensing, highlighting its growing importance and the challenges it faces. Deep learning, characterized by neural networks with multiple layers, has proven effective in various fields, including image analysis, object detection, and semantic segmentation. In remote sensing, deep learning is being used to address unique challenges such as multi-modal data fusion, geo-located data, geodetic measurements, time-series processing, and big data. The authors discuss the development of deep learning models, including autoencoders, restricted Boltzmann machines, and convolutional neural networks (CNNs), and their applications in hyperspectral image analysis, synthetic aperture radar (SAR) image interpretation, high-resolution satellite image interpretation, multimodal data fusion, and 3D reconstruction. They also provide resources for scientists interested in exploring deep learning in remote sensing, emphasizing the need for remote sensing experts to integrate their knowledge into deep learning models to tackle large-scale challenges like climate change and urbanization.This article reviews the application of deep learning in remote sensing, highlighting its growing importance and the challenges it faces. Deep learning, characterized by neural networks with multiple layers, has proven effective in various fields, including image analysis, object detection, and semantic segmentation. In remote sensing, deep learning is being used to address unique challenges such as multi-modal data fusion, geo-located data, geodetic measurements, time-series processing, and big data. The authors discuss the development of deep learning models, including autoencoders, restricted Boltzmann machines, and convolutional neural networks (CNNs), and their applications in hyperspectral image analysis, synthetic aperture radar (SAR) image interpretation, high-resolution satellite image interpretation, multimodal data fusion, and 3D reconstruction. They also provide resources for scientists interested in exploring deep learning in remote sensing, emphasizing the need for remote sensing experts to integrate their knowledge into deep learning models to tackle large-scale challenges like climate change and urbanization.