  In this work, we present the application of convolutional neural networks for
segmenting water bodies in satellite images. We first use a variant of the
U-Net model to segment rivers and lakes from very high-resolution images from
Peru. To circumvent the issue of scarce labelled data, we investigate the
applicability of a knowledge transfer-based model that learns the mapping from
high-resolution labelled images and combines it with the very high-resolution
mapping so that better segmentation can be achieved. We train this model in a
single process, end-to-end. Our preliminary results show that adding the
information from the available high-resolution images does not help
out-of-the-box, and in fact worsen results. This leads us to infer that the
high-resolution data could be from a different distribution, and its addition
leads to increased variance in our results.
