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

Graph-based Active Learning for Surface Water and Sediment Detection in Multispectral Images