Abstract-Owing to the recent development of sensor resolutions on-board different Earth observation platforms, remote sensing is an important source of information for mapping and monitoring natural and man-made land covers. Of particular importance is the increasing amounts of available hyperspectral data originating from airborne and satellite sensors such as AVIRIS, HyMap, and Hyperion with very high spectral resolution (i.e., high number of spectral channels) containing rich information for a wide range of applications. A relevant example is the separation of different types of land cover classes using this data in order to understand for example impacts of natural disasters or changing of city buildings over time. More recently, such increases in the data volume, velocity, and variety of data contributed to the term "big data" that stands for challenges shared with many other scientific disciplines. On the one hand the amount of available data is increasing in a way that raises the demand for automatic data analysis elements since many of the available data collections are massively underutilized lacking experts for manual investigation. On the other hand, proven statistical methods (e.g.,, dimensionality reduction) driven by manual approaches have a significant impact in reducing the amount of "big data" towards smaller "smart data" contributing to the more recently used terms data value and veracity (i.e., less noise, lower dimensions that capture the most important information). This paper aims to take stock of which proven statistical data mining methods in remote sensing are used to contribute to "smart data" analysis processes in the light of possible automation as well as scalable and parallel processing techniques. We focus on parallel support vector machines (SVMs) as one of the best out-of-the-box classification methods.

One of the observations made in earth data science is the massive increase of data volume (e.g, higher resolution measurements) and dimensionality (e.g. hyper-spectral bands). Traditional data mining tools (Matlab, R, etc.) are becoming redundant in the analysis of these datasets, as they are unable to process or even load the data. Parallel and scalable techniques, though, bear the potential to overcome these limitations. In this contribution we therefore evaluate said techniques in a High Performance Computing (HPC) environment on the basis of two earth science case studies: (a) Density-based Spatial Clustering of Applications with Noise (DBSCAN) for automated outlier detection and noise reduction in a 3D point cloud and (b) land cover type classification using multi-class Support Vector Machines (SVMs) in multispectral satellite images. The paper compares implementations of the algorithms in traditional data mining tools with HPC realizations and 'big data' technology stacks. Our analysis reveals that a wide variety of them are not yet suited to deal with the coming challenges of data mining tasks in earth sciences.

For the next generation of very high throughput communication satellites, free-space optical (FSO) communication between ground stations and geostationary telecommunication satellites is likely to replace conventional RF links. To mitigate atmospheric turbulence, TNO and DLR propose Adaptive Optics (AO) to apply uplink pre-correction. In order to demonstrate the feasibility of AO pre-correction an FSO link has been tested over a 10 km range. This paper shows that AO pre-correction is most advantageous for low point ahead angles (PAAs), as expected. In addition, an optimum AO precorrection performance is found at 16 AO modes for the experimental conditions. For the specific test site, tip-tilt precorrection accounted for 4.5 dB improvement in the link budget. Higher order AO modes accounted for another 1.5 dB improvement in the link budget. From these results it is concluded that AO pre-correction can effectively improve highthroughput optical feeder links.

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