  Deep learning (DL) based supervised change detection (CD) models require
large labeled training data. Due to the difficulty of collecting labeled
multi-temporal data, unsupervised methods are preferred in the CD literature.
However, unsupervised methods cannot fully exploit the potentials of
data-driven deep learning and thus they are not absolute alternative to the
supervised methods. This motivates us to look deeper into the supervised DL
methods and investigate how they can be adopted intelligently for CD by
minimizing the requirement of labeled training data. Towards this, in this work
we show that geographically diverse training dataset can yield significant
improvement over less diverse training datasets of the same size. We propose a
simple confidence indicator for verifying the trustworthiness/confidence of
supervised models trained with small labeled dataset. Moreover, we show that
for the test cases where supervised CD model is found to be less
confident/trustworthy, unsupervised methods often produce better result than
the supervised ones.
