Scaling Efficient Masked Autoencoder Learning on Large Remote Sensing
Dataset
Scaling Efficient Masked Autoencoder Learning on Large Remote Sensing
Dataset
Masked Image Modeling (MIM) has emerged as a pivotal approach for developing foundational visual models in the field of remote sensing (RS). However, current RS datasets are limited in volume and diversity, which significantly constrains the capacity of MIM methods to learn generalizable representations. In this study, we introduce \textbf{RS-4M}, …