  Unmanned aerial vehicles (UAVs) are frequently used for aerial mapping and
general monitoring tasks. Recent progress in deep learning enabled automated
semantic segmentation of imagery to facilitate the interpretation of
large-scale complex environments. Commonly used supervised deep learning for
segmentation relies on large amounts of pixel-wise labelled data, which is
tedious and costly to annotate. The domain-specific visual appearance of aerial
environments often prevents the usage of models pre-trained on publicly
available datasets. To address this, we propose a novel general planning
framework for UAVs to autonomously acquire informative training images for
model re-training. We leverage multiple acquisition functions and fuse them
into probabilistic terrain maps. Our framework combines the mapped acquisition
function information into the UAV's planning objectives. In this way, the UAV
adaptively acquires informative aerial images to be manually labelled for model
re-training. Experimental results on real-world data and in a photorealistic
simulation show that our framework maximises model performance and drastically
reduces labelling efforts. Our map-based planners outperform state-of-the-art
local planning.
