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Accurate scene understanding from multiple sensors mounted on cars is a keyrequirement for autonomous driving systems. Nowadays, this task is mainlyperformed through data-hungry deep learning techniques that need very largeamounts of data to be trained. Due to the high cost of performing segmentationlabeling, many synthetic datasets have been proposed. However, most of themmiss the multi-sensor nature of the data, and do not capture the significantchanges introduced by the variation of daytime and weather conditions. To fillthese gaps, we introduce SELMA, a novel synthetic dataset for semanticsegmentation that contains more than 30K unique waypoints acquired from 24different sensors including RGB, depth, semantic cameras and LiDARs, in 27different atmospheric and daytime conditions, for a total of more than 20Msamples. SELMA is based on CARLA, an open-source simulator for generatingsynthetic data in autonomous driving scenarios, that we modified to increasethe variability and the diversity in the scenes and class sets, and to align itwith other benchmark datasets. As shown by the experimental evaluation, SELMAallows the efficient training of standard and multi-modal deep learningarchitectures, and achieves remarkable results on real-world data. SELMA isfree and publicly available, thus supporting open science and research.

SELMA: SEmantic Large-scale Multimodal Acquisitions in Variable Weather, Daytime and Viewpoints