  Data from satellites or aerial vehicles are most of the times unlabelled.
Annotating such data accurately is difficult, requires expertise, and is costly
in terms of time. Even if Earth Observation (EO) data were correctly labelled,
labels might change over time. Learning from unlabelled data within a
semi-supervised learning framework for segmentation of aerial images is
challenging. In this paper, we develop a new model for semantic segmentation of
unlabelled images, the Non-annotated Earth Observation Semantic Segmentation
(NEOS) model. NEOS performs domain adaptation as the target domain does not
have ground truth semantic segmentation masks. The distribution inconsistencies
between the target and source domains are due to differences in acquisition
scenes, environment conditions, sensors, and times. Our model aligns the
learned representations of the different domains to make them coincide. The
evaluation results show that NEOS is successful and outperforms other models
for semantic segmentation of unlabelled data.
