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Semantic segmentation is a crucial task for robot navigation and safety.However, it requires huge amounts of pixelwise annotations to yield accurateresults. While recent progress in computer vision algorithms has been heavilyboosted by large ground-level datasets, the labeling time has hampered progressin low altitude UAV applications, mostly due to the difficulty imposed by largeobject scales and pose variations. Motivated by the lack of a large videoaerial dataset, we introduce a new one, with high resolution (4K) images andmanually-annotated dense labels every 50 frames. To help the video labelingprocess, we make an important step towards automatic annotation and proposeSegProp, an iterative flow-based method with geometric constrains to propagatethe semantic labels to frames that lack human annotations. This results in adataset with more than 50k annotated frames - the largest of its kind, to thebest of our knowledge. Our experiments show that SegProp surpasses currentstate-of-the-art label propagation methods by a significant margin.Furthermore, when training a semantic segmentation deep neural net using theautomatically annotated frames, we obtain a compelling overall performanceboost at test time of 16.8% mean F-measure over a baseline trained only withmanually-labeled frames.  Our Ruralscapes dataset, the label propagation code and a fast segmentationtool are available at our website:https://sites.google.com/site/aerialimageunderstanding/

Towards Automatic Annotation for Semantic Segmentation in Drone Videos