  Semantic segmentation plays a fundamental role in a broad variety of computer
vision applications, providing key information for the global understanding of
an image. Yet, the state-of-the-art models rely on large amount of annotated
samples, which are more expensive to obtain than in tasks such as image
classification. Since unlabelled data is instead significantly cheaper to
obtain, it is not surprising that Unsupervised Domain Adaptation reached a
broad success within the semantic segmentation community.
  This survey is an effort to summarize five years of this incredibly rapidly
growing field, which embraces the importance of semantic segmentation itself
and a critical need of adapting segmentation models to new environments. We
present the most important semantic segmentation methods; we provide a
comprehensive survey on domain adaptation techniques for semantic segmentation;
we unveil newer trends such as multi-domain learning, domain generalization,
test-time adaptation or source-free domain adaptation; we conclude this survey
by describing datasets and benchmarks most widely used in semantic segmentation
research. We hope that this survey will provide researchers across academia and
industry with a comprehensive reference guide and will help them in fostering
new research directions in the field.
