Kheng Cher Yeo

CADE: A lightweight change-adaptive dual-path encoder framework for predicting and generating future changes in satellite imagery using semi-supervised learning

The findings indicate that CADE is precise and computationally efficient, showing that it is suitable for low-resource remote sensing uses and minimizes dependence on extensive annotated datasets as well.