Mars_seg

In the Mars exploration mission, it is necessary to construct a series of datasets in the Martian environment for the training and validation of relevant perception algorithms. In 2021, NASA's Mars Science Laboratory released the first large-scale Martian terrain dataset called AI4Mars for semantic segmentation tasks, which includes four types of terrain. However, due to the lack of defined terrain categories and low annotation accuracy, the dataset lacks practical application value. For this purpose, our team has released a series of datasets named Marsnseg for semantic segmentation tasks.The Mars seg dataset contains high-resolution images of rich Martian scenes, which helps researchers understand the true Martian landscape. All single channel grayscale images in this dataset are from the Planetary Data System (PDS), covering 1064 high-definition images captured by the Navigation Camera (NAVCAM) and Panoramic Camera (PANCAM) of the Opportunity and Courage Mars rovers (MER); All RGB images were collected by Mars 32k, all from the MastCam camera of the Curiosity rover (MSL), with a total of 4148 images. Among them, the spatial resolution of grayscale images in MER Seg is 1024 × 1024, while color images in MSL Seg are downsampled to 560 × 500 through bilinear interpolation.In this dataset, by analyzing the difficulties encountered by the rover during the detection process and the high-risk issues that may be encountered during the task execution, we divide the terrain in the dataset into the following 9 categories.在火星探测任务中，需要构建火星场景下的系列数据集用于相关感知算法的训练以及验证。2021年，围绕语义分割任务中，美国宇航局火星科学实验室发布了第一个名为AI4Mars的大规模火星地形数据集，其中包含四种类型的地形。但由于其中的地形类别定义匮乏，标注精度相对较低，使得数据集的实用价值有限。为此，本团队针对语义分割任务，发布了一组命名为Mars_seg的系列数据集。​ Mars- seg数据集包含丰富的火星场景的高分辨率图像，这有助于研究人员了解真正的火星景观。该数据集的所有单通道灰度图像均来自行星数据系统(PDS)，覆盖了机遇号和勇气号火星漫游者(MER)的导航摄像机(NAVCAM)和全景摄像机(PANCAM)拍摄的1064张高清图像；所有的RGB图像是由火星32k收集的，全部来自好奇号漫游者(MSL)的桅杆照相机(MastCam)，总共4148幅图像。其中，MER-Seg中的灰度图像的空间分辨率是1024 × 1024，而MSL-Seg中的彩色图像通过双线性插值被降采样到560 × 500。​ 在本数据集中，通过分析探测车在探测过程中遇到的困难和执行任务过程中可能遇到的高风险问题，我们将数据集中的地形划分为以下9个类别。