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The continuous progress in remote sensor resolutions of Earth observation platforms generates large quantities of hyperspectral data for the mapping and monitoring of natural and man-made land covers.

The continuous progress in remote sensor resolutions of Earth observation platforms generates large quantities of hyperspectral data for the mapping and monitoring of natural and man-made land covers. Current Synthetic Aperture Radar missions – with high spatial resolution and frequent repeat passes – raise huge requirements for the analysis of satellite time series data. This enables the observation and analysis of dynamic processes involving natural landscape and built-up sites with significant socio-economic, environmental, and geopolitical impact. Similarly, 3D point cloud datasets in earth sciences created by 3D laser scanners drive data growth, up to scans of whole countries.

The University of Iceland team provides an application based on three data analytics methods used in order to extract knowledge:
•    clustering (HPDBSCAN),
•    classification (piSVM),
•    and a variety of Deep Learning frameworks (such as TensorFlow, Theano, Caffe, CNTK, or Torch).

 

Data Analytics in DEEP-EST

This data analytics in Earth Science application explores innovative parallel I/O methods using the DEEP-EST NAM devices, and will adapt data analytics codes to leverage the DEEP-EST MSA. A port of a suitable Deep Learning network will be performed as a joint activity with other DEEP-EST partners, with the DEEP-EST DAM being the main target. The requirements of the three principal data analytics techniques mentioned above will be injected into the co-design effort of DEEP-EST.

All three algorithms (HPDBSCAN, piSVM, and deep neural network learning) will utilise the data analytics module (DAM).
•    The hyperspectral remote sensing data will be sent to the NAM.
•    The overall satellite dataset and large point cloud data residing on the DEEP-EST SSSM.

Goal

The expected outcome is reduced time to solution for classification, clustering and deep learning applications, including the search for correct parameters through cross-validation, and a significant speed-up with respect to standard parallel versions due to the adoption of the MSA. The innovative integrated computing and data architecture of DEEP-EST will contribute to cutting edge knowledge discovery with unprecedented effectiveness and efficiency.