This repository implements a plain vision transformer adapted for remote sensing imagery, advancing foundation model capabilities for aerial and satellite image understanding. It supports multiple downstream vision tasks including object detection in aerial images, semantic segmentation, and scene classification across benchmark datasets like DOTA, DIOR-R, UCM, and AID. The approach includes self-supervised pretraining and transfer learning strategies to leverage large-scale unlabeled remote sensing data.