  Unmanned aerial vehicles combined with computer vision systems, such as
convolutional neural networks, offer a flexible and affordable solution for
terrain monitoring, mapping, and detection tasks. However, a key challenge
remains the collection and annotation of training data for the given sensors,
application, and mission. We introduce an informative path planning system that
incorporates novelty estimation into its objective function, based on research
for uncertainty estimation in deep learning. The system is designed for data
collection to reduce both the number of flights and of annotated images. We
evaluate the approach on real world terrain mapping data and show significantly
smaller collected training dataset compared to standard lawnmower data
collection techniques.
