  Semantic segmentation is key in autonomous driving. Using deep visual
learning architectures is not trivial in this context, because of the
challenges in creating suitable large scale annotated datasets. This issue has
been traditionally circumvented through the use of synthetic datasets, that
have become a popular resource in this field. They have been released with the
need to develop semantic segmentation algorithms able to close the visual
domain shift between the training and test data. Although exacerbated by the
use of artificial data, the problem is extremely relevant in this field even
when training on real data. Indeed, weather conditions, viewpoint changes and
variations in the city appearances can vary considerably from car to car, and
even at test time for a single, specific vehicle. How to deal with domain
adaptation in semantic segmentation, and how to leverage effectively several
different data distributions (source domains) are important research questions
in this field. To support work in this direction, this paper contributes a new
large scale, synthetic dataset for semantic segmentation with more than 100
different source visual domains. The dataset has been created to explicitly
address the challenges of domain shift between training and test data in
various weather and view point conditions, in seven different city types.
Extensive benchmark experiments assess the dataset, showcasing open challenges
for the current state of the art. The dataset will be available at:
https://idda-dataset.github.io/home/ .

Computer Vision and Pattern Recognition;Robotics