  Change detection (CD) is an essential task for various real-world
applications, such as urban management and disaster assessment. However,
previous methods primarily focus on improving the accuracy of CD, while
neglecting the reliability of detection results. In this paper, we propose a
novel change detection network, called AR-CDNet, which is able to provide
accurate change maps and generate pixel-wise uncertainty. Specifically, an
online uncertainty estimation branch is constructed to model the pixel-wise
uncertainty, which is supervised by the difference between predicted change
maps and corresponding ground truth during the training process. Furthermore,
we introduce a knowledge review strategy to distill temporal change knowledge
from low-level features to high-level ones, thereby enhancing the
discriminability of temporal difference features. Finally, we aggregate the
uncertainty-aware features extracted from the online uncertainty estimation
branch with multi-level temporal difference features to improve the accuracy of
CD. Once trained, our AR-CDNet can provide accurate change maps and evaluate
pixel-wise uncertainty without ground truth. Experimental results on two
benchmark datasets demonstrate the superior performance of AR-CDNet in the CD
task. The demo code for our work will be publicly available at
\url{https://github.com/guanyuezhen/AR-CDNet}.
