  Satellite imagery is crucial for tasks like environmental monitoring and
urban planning. Typically, it relies on semantic segmentation or Land Use Land
Cover (LULC) classification to categorize each pixel. Despite the advancements
brought about by Deep Neural Networks (DNNs), their performance in segmentation
tasks is hindered by challenges such as limited availability of labeled data,
class imbalance and the inherent variability and complexity of satellite
images. In order to mitigate those issues, our study explores the effectiveness
of a Cut-and-Paste augmentation technique for semantic segmentation in
satellite images. We adapt this augmentation, which usually requires labeled
instances, to the case of semantic segmentation. By leveraging the connected
components in the semantic segmentation labels, we extract instances that are
then randomly pasted during training. Using the DynamicEarthNet dataset and a
U-Net model for evaluation, we found that this augmentation significantly
enhances the mIoU score on the test set from 37.9 to 44.1. This finding
highlights the potential of the Cut-and-Paste augmentation to improve the
generalization capabilities of semantic segmentation models in satellite
imagery.
