RSE Special Issue - Big Remotely Sensed Data: tools, applications and experiences

by — 2016-03-02 16:17:49

The advent of new generation satellite systems, including the Copernicus Sentinel constellations (Sentinel-1, Sentinel-2), are driving the development of new large scale data handling approaches in Remote Sensing related disciplines. Moreover, the huge data archives collected over the last decades by several past missions, such as ERS-1/2, ENVISAT, RadarSAT-1/2, TerraSAR-X, Cosmo-SkyMED, Landsat, SPOT and some EOS systems such as ASTER, not originally intended for single-task global processing, are now being used to produce innovative global products.

In particular, the development of large-scale analytics tools to efficiently extract information and apply the achieved results towards answering scientific questions represents a big challenge for the research community working in the Remote Sensing field. The scope of the challenges faced by the community is not just restricted to improving the capacity to collect data but also includes developing the appropriate algorithms, tools and platforms needed to store, analyze, interpret and represent data and results.

This special issue brings together experts on data science, algorithm development and computer science, as well as environmental engineers and geoscientists, to present state-of-the-art algorithms, tools, and applications oriented to processing and exploitation of a huge amount of remotely sensed data. The issue welcomes (i) studies describing advanced approaches to process large volume of multi-temporal SAR (Synthetic Aperture Radar), optical and radiometric data, (ii) studies discussing innovative techniques, and associated data processing methods for very large-scale data exploitation, (iii) critical analyses of existing and innovative tools, methods and techniques for large-scale analytics to extract and represent information, (iv) results of case studies executed at different large spatial and temporal scales, also by using GRID and/or Cloud Computing platforms, and (v) results of on-going national/international initiatives and solutions for managing, processing, and disseminating huge archives of Remote Sensing data and relevant results.

Submissions are due by July 1, 2016. Submit your paper under the category "Big Remotely Sensed Data SI" using RSE's online submission system. Follow the author instructions in preparing your paper.