Learning from Unlabelled Data with Transformers: Domain Adaptation for Semantic Segmentation of High Resolution Aerial Images

arXiv:2404.11299v1 Announce Type: new
Abstract: 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 …

abstract aerial arxiv change cs.cv cs.lg data domain domain adaptation earth earth observation expertise images labels observation resolution satellites segmentation semantic terms the times transformers type vehicles