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

This dataset was built for training and validating terrain classification models for Mars, which may be useful in future autonomous rover efforts. It consists 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.

本数据集专为火星地形分类模型的训练与验证构建，可助力未来自主漫游车探测任务。
数据集包含来自好奇号（Curiosity）、机遇号（Opportunity）以及勇气号（Spirit）漫游车的3.5万张图像，配套约32.6万张语义分割（semantic segmentation）全图标签，所有标签均通过众包方式采集。为保障众包标签的标注质量与一致性，每张图像均由10名标注者完成标注。
此外，本数据集还包含约1500张验证标签，这些标签由美国国家航空航天局（NASA）下属火星科学实验室（Mars Science Laboratory, MSL）任务与火星探测漫游者（Mars Exploration Rovers, MER）任务的漫游车规划师及科学家标注完成；其中MSL任务负责运营好奇号漫游车，MER任务则曾运营勇气号与机遇号漫游车。