  Training Convolutional Neural Networks (CNNs) for very high resolution images
requires a large quantity of high-quality pixel-level annotations, which is
extremely labor- and time-consuming to produce. Moreover, professional photo
interpreters might have to be involved for guaranteeing the correctness of
annotations. To alleviate such a burden, we propose a framework for semantic
segmentation of aerial images based on incomplete annotations, where annotators
are asked to label a few pixels with easy-to-draw scribbles. To exploit these
sparse scribbled annotations, we propose the FEature and Spatial relaTional
regulArization (FESTA) method to complement the supervised task with an
unsupervised learning signal that accounts for neighbourhood structures both in
spatial and feature terms.
