The field of remote sensing and hyperspectral image analysis is rapidly advancing with the development of new deep learning methods and techniques. One of the major trends in this field is the use of domain generalization and adaptation methods to improve the robustness and accuracy of models in diverse environments and scenarios. Researchers are also exploring the use of novel architectures such as transformers and mixture-of-experts models to improve the performance of hyperspectral image classification and object detection tasks. Another area of focus is the development of efficient and effective methods for few-shot learning and adaptation, which is critical for real-world applications where labeled data may be limited. Notably, papers such as DG-DETR and SA-DETR have made significant contributions to domain generalized object detection, while MambaMoE has achieved state-of-the-art performance in hyperspectral image classification. Furthermore, the comprehensive survey provided by Vision Mamba in Remote Sensing has laid a structured foundation for advancing research in remote sensing systems through state space model-based methods.