Currently, Martian water–ice cloud properties, such as wind direction and angular wind velocity, are determined through manual analysis of atmospheric movies taken by the Mars Science Laboratory (MSL, Curiosity). These atmospheric movies, known as Zenith Movies (ZM), have a vertical elevational pointing which allows a direct measurement of wind properties from overhead clouds. However, acquiring this observation requires a significant amount of downlinked data volume which impedes on how often it can be taken. To combat this, an algorithm using Computer Vision (CV) and machine learning has been developed to calculate cloud parameters directly.

To determine how well the algorithm performs, it has been tested on a previous data set from Campbell et al. (2020) that manually measured the wind direction and angular distance in ZMs. This data set had a variety of movies with different cloud features. When ZMs had strong features, the algorithm matched well with manual results which shows promising results. However, movies that had either lighting changes, multiple cloud decks or camera artifacts caused the algorithm to perform less well. Therefore the algorithm needs improving to more accurately measure these parameters over an assortment of conditions.

目前，火星水冰云的属性，例如风向和角风速，是通过对火星科学实验室拍摄的大气电影进行手动分析确定的（MSL，好奇号）。这些被称为天顶电影（ZM）的大气电影具有垂直的仰角指向，可以直接测量高空云的风属性。但是，获取此观测值需要大量的下行数据量，这阻碍了可以多久获取一次。为了解决这个问题，已经开发了一种使用计算机视觉（CV）和机器学习的算法来直接计算云参数。

为了确定算法的性能，已在Campbell等人的先前数据集上对其进行了测试。（2020年）以ZMs手动测量风向和角距离。该数据集包含具有不同云功能的各种电影。当ZM具有强大的功能时，该算法与人工结果非常吻合，显示出令人鼓舞的结果。但是，具有灯光变化，多个云平台或摄影机伪影的电影使算法的性能下降。因此，算法需要改进以在各种条件下更准确地测量这些参数。