  Today deep convolutional neural networks (CNNs) push the limits for most
computer vision problems, define trends, and set state-of-the-art results. In
remote sensing tasks such as object detection and semantic segmentation, CNNs
reach the SotA performance. However, for precise performance, CNNs require much
high-quality training data. Rare objects and the variability of environmental
conditions strongly affect prediction stability and accuracy. To overcome these
data restrictions, it is common to consider various approaches including data
augmentation techniques. This study focuses on the development and testing of
object-based augmentation. The practical usefulness of the developed
augmentation technique is shown in the remote sensing domain, being one of the
most demanded ineffective augmentation techniques. We propose a novel pipeline
for georeferenced image augmentation that enables a significant increase in the
number of training samples. The presented pipeline is called object-based
augmentation (OBA) and exploits objects' segmentation masks to produce new
realistic training scenes using target objects and various label-free
backgrounds. We test the approach on the buildings segmentation dataset with
six different CNN architectures and show that the proposed method benefits for
all the tested models. We also show that further augmentation strategy
optimization can improve the results. The proposed method leads to the
meaningful improvement of U-Net model predictions from 0.78 to 0.83 F1-score.
