Datasets

We build our experiments on Three Urban driving datasets, one being synthetic, and the others being recorded in various geographic locations. We standardize the label set with 7 superclasses, common to all datasets:  [Flat, construction, object, nature, sky, human, and vehicle].

0 1- G T A 5 ( G R A N D T H E F T AU T O 5 ) D A T A S E T :

is a dataset of 24966 synthetic images sized 52 GB and 5000 synthetic images, sized 10 GB, the images have been rendered using the open-world video game Grand Theft Auto 5 and are all from the car perspective in the streets. The dataset is synthetic images with pixel-level semantic annotation with 19 classes that are compatible with the ones of the Cityscapes dataset in American-style virtual cities.

02- C I T Y S C A P E S D A T A S E T :

contains labeled urban scenes from 50 cities in Europe, split in training and validation sets of 5000 annotated images with fine annotations and 20 000 annotated images with coarse annotations, with 35 classes such as humans, cars, road, sky, etc. It has a large number of dynamic objects and several seasons (spring, summer, and fall) by Varying scene layout and background.

03- M A P I L L A R Y D A T A S E T:

is a dataset that is 5x larger than the total amount of fine annotations for Cityscapes and contains images from 190 countries around the world, it is composed of 25,000 high-resolution images annotated in a dense and finegrained style by using polygons for delineating individual objects, with 66 object categories with additional, instance-specific labels for 37 classes. Though all contain urban scenes, the datasets have different labeling policies and semantic granularity. We standardize the label set with 7 superclasses, common to all datasets: flat, construction, object, nature, sky, human, and vehicle.