AI4MARS: A Dataset for Terrain-Aware Autonomous Driving on Mars

R. Michael Swan, Deegan Atha, Henry A. Leopold, Matthew Gildner, Stephanie Oij, Cindy Chiu, Masahiro Ono; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2021, pp. 1982-1991

Deep learning has quickly become a necessity for self-driving vehicles on Earth. In contrast, the self-driving vehicles on Mars, including NASA's latest rover, Perseverance, which is planned to land on Mars in February 2021, are still driven by classical machine vision systems. Deep learning capabilities, such as semantic segmentation and object recognition, would substantially benefit the safety and productivity of ongoing and future missions to the red planet. To this end, we created the first large-scale dataset, AI4Mars, for training and validating terrain classification models for Mars, consisting of 326K semantic segmentation full image labels on 35K images from Curiosity, Opportunity, and Spirit rovers, collected through crowdsourcing. Each image was labeled by 10 people to ensure greater quality and agreement of the crowdsourced labels. It also includes 1.5K validation labels annotated by the rover planners and scientists from NASA's MSL (Mars Science Laboratory) mission, which operates the Curiosity rover, and MER (Mars Exploration Rovers) mission, which operated the Spirit and Opportunity rovers. We trained a DeepLabv3 model on the AI4Mars training dataset and achieved over 96% overall classification accuracy on the test set. The dataset is made publicly available.