Open access
Sep 2026
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.
Debopom Sutradhar, Md. Abdur Rahman, Kheng Cher Yeo et al.
· Neural computing & applicati... · 0 citations