Given a short query with keyphrases about a research idea in 2020 in the domain of artificial intelligence, recommend the relevant datasets to validate the research idea from options provided. A short description of the datasets is also provided to help you.

Query: I want to train a semantic segmentation model for RGB images.

Keyphrases: semantic segmentation rgb

Dataset description:
SUN3D: SUN3D contains a large-scale RGB-D video database, with 8 annotated sequences. Each frame has a semantic segmentation of the objects in the scene and information about the camera pose.
ScanNet: ScanNet is an instance-level indoor RGB-D dataset that includes both 2D and 3D data. It is a collection of labeled voxels rather than points or objects.
Cityscapes: Cityscapes is a large-scale database which focuses on semantic understanding of urban street scenes. It provides semantic, instance-wise, and dense pixel annotations for 30 classes grouped into 8 categories (flat surfaces, humans, vehicles, constructions, objects, nature, sky, and void).
INRIA Aerial Image Labeling: The INRIA Aerial Image Labeling dataset is comprised of 360 RGB tiles of 5000×5000px with a spatial resolution of 30cm/px on 10 cities across the globe. Half of the cities are used for training and are associated to a public ground truth of building footprints.

Options:- A: SUN3D  B: ScanNet  C: Cityscapes  D: INRIA Aerial Image Labeling
Cityscapes