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Semantic segmentation is a crucial task for robot navigation and safety.However, current supervised methods require a large amount of pixelwiseannotations to yield accurate results. Labeling is a tedious and time consumingprocess that has hampered progress in low altitude UAV applications. This papermakes an important step towards automatic annotation by introducing SegProp, anovel iterative flow-based method, with a direct connection to spectralclustering in space and time, to propagate the semantic labels to frames thatlack human annotations. The labels are further used in semi-supervised learningscenarios. Motivated by the lack of a large video aerial dataset, we alsointroduce Ruralscapes, a new dataset with high resolution (4K) images andmanually-annotated dense labels every 50 frames - the largest of its kind, tothe best of our knowledge. Our novel SegProp automatically annotates theremaining unlabeled 98% of frames with an accuracy exceeding 90% (F-measure),significantly outperforming other state-of-the-art label propagation methods.Moreover, when integrating other methods as modules inside SegProp's iterativelabel propagation loop, we achieve a significant boost over the baselinelabels. Finally, we test SegProp in a full semi-supervised setting: we trainseveral state-of-the-art deep neural networks on theSegProp-automatically-labeled training frames and test them on completely novelvideos. We convincingly demonstrate, every time, a significant improvement overthe supervised scenario.

Semantics through Time: Semi-supervised Segmentation of Aerial Videos with Iterative Label Propagation