Our goal is to employ vomputer vision and data science methods to advance the data handling, analyses and interpretation of the wealth of Big Data 3-D remotely sensed environmental data acquired by the AWI on polar expeditions. Objective: Data acquisitions from drone-borne and airplane passive and active optical imaging sensors over large areas in Siberia and Alaska resulted in multi-temporal datasets of billions of remotely sensed points in spatially explicit point clouds. Computer vision already enables notable advancements in 2-D Big Data environmental data science. In addition, a wide range of 3-D sensors is employed to investigate polar terrestrial environments and permafrost landscapes: ground-based, drone-borne, and airplane active optical laser scanning devices and passive optical densely overlapping imaging from different view angles provide 3-D point cloud data consisting of billions of individual measurements of surface structures, including vegetation and permafrost landscape topography in the circumpolar region. Approach: 3-D point clouds containing billions of remotely sensed points over large areas are the products from the drone-borne and airplane LIDAR as well as high-resolution Red Green Blue and Red Green Near Infrared cameras that allow stereo photogrammetric derivation of point clouds. We need machine learning on the Big Data 3-D point clouds that will also allow us to analyse the characteristics and interactions among the points in 3-D space to enable recognition of tree species and terrestrial degradation features (classification), to enable segmentation to identify the meaning of the environmental objects, and to develop advanced automated change detection tools for multi-temporal point cloud datasets. We will apply two use cases using high latitude biodiversity and permafrost landscape diversity.