Few-Shot Learning in Remote Sensing: Trends, Gaps, and Future Directions

This article outlines major trends and research gaps in few-shot learning for remote sensing, highlighting evolving methods like graph networks, ViTs, and meta-learning. It also emphasizes the need for real-time, computationally efficient models—especially for UAV applications.