  Remote sensing through semantic segmentation of satellite images contributes
to the understanding and utilisation of the earth's surface. For this purpose,
semantic segmentation networks are typically trained on large sets of labelled
satellite images. However, obtaining expert labels for these images is costly.
Therefore, we propose to rely on a low-cost approach, e.g. crowdsourcing or
pretrained networks, to label the images in the first step. Since these initial
labels are partially erroneous, we use active learning strategies to
cost-efficiently refine the labels in the second step. We evaluate the active
learning strategies using satellite images of Bengaluru in India, labelled with
land cover and land use labels. Our experimental results suggest that an active
label refinement to improve the semantic segmentation network's performance is
beneficial.
