  We develop a graph active learning pipeline (GAP) to detect surface water and
in-river sediment pixels in satellite images. The active learning approach is
applied within the training process to optimally select specific pixels to
generate a hand-labeled training set. Our method obtains higher accuracy with
far fewer training pixels than both standard and deep learning models.
According to our experiments, our GAP trained on a set of 3270 pixels reaches a
better accuracy than the neural network method trained on 2.1 million pixels.
