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<div id="divRplyFwdMsg" dir="ltr"><font face="Calibri, sans-serif" style="font-size:11pt" color="#000000"><b>From:</b> Gregory Giuliani <gregory.giuliani@unige.ch><br>
<b>Sent:</b> 30 November 2018 14:30<br>
<b>Subject:</b> [reminder] Special Issue "Earth Observation Data Cubes" - Call for Papers</font>
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<div class="" style="word-wrap:break-word; line-break:after-white-space">Dear colleagues,
<div class=""><br class="">
</div>
<div class="">Just a quick reminder about the on-going call for papers on EO Data Cubes"<br class="">
<br class="">
Remotely sensed Earth Observations (EO) data have already exceeded the petabyte-scale and are increasingly freely and openly available from different data holdings. This poses a certain number of issues in terms of volume (e.g., data volumes have increased 10x
in the last 5 years); velocity (e.g., Sentinel-2 is capturing a new image of any given place every 5 days); and variety (e.g., different types of sensors, spatial/spectral resolutions). Traditional approaches to the acquisition, management, distribution and analysis
of EO data have limitations (e.g., data size, heterogeneity and complexity) that impede their true information potential to be realized.<br class="">
<br class="">
The fact that the full information potential of EO data has not yet been realized and therefore remains still underutilized is explained by various reasons: (1) It requires scientific knowledge to understand what data is needed
optical (which resolution?),
radar (which type?); (2) It is difficult to access and download the increasing volumes of data generated by satellites; (3) there is a lack of expertise and computing resources to efficiently prepare and utilize EO data; (4) the particular structure of EO
data; and (5) the significant effort and cost required to store and process data limit its effective use.<br class="">
<br class="">
Addressing Big Data challenges such as volume, velocity and variety, requires a change of paradigm and moving away from traditional local processing and data distribution methods to lower the barriers caused by data size and related complications in data management.
In particular, data volume and velocity will continue to grow as the demands increase for decision-support information derived from these data.<br class="">
<br class="">
To tackle these issues and bridge the gap between users expectations and current Big Data analytical capabilities, EO Data Cubes (EODC) are a new paradigm revolutionizing the way users can interact with EO data and a promising solution to store, organize, manage
and analyze EO data. The main objective of EODC is to facilitate EO data usage by addressing volume, velocity, variety challenges and providing access to large spatio-temporal data in an analysis ready format.<br class="">
<br class="">
Different EODC implementations are currently operational such as Digital Earth Australia, the Swiss Data Cube, the EarthServer, the E-sensing platform or the Google Earth Engine. These initiatives are paving the way to broaden the use of EO data to larger communities
of users; support decision-makers with timely and actionable information converted into meaningful geophysical variables; and ultimately are unlocking the information power of EO data.<br class="">
<br class="">
This Special Issue is consequently aiming to cover the most recent advances in EODC developments and implementations and welcomes contributions with respect to (but without being restricted to):<br class="">
<br class="">
<span class="x_Apple-tab-span" style="white-space:pre"></span> Methods for generating Analysis Ready Data for both optical and SAR imagery<br class="">
<span class="x_Apple-tab-span" style="white-space:pre"></span> Interoperability challenges between EO Data Cubes<br class="">
<span class="x_Apple-tab-span" style="white-space:pre"></span> Algorithms for generating decision-ready products<br class="">
<span class="x_Apple-tab-span" style="white-space:pre"></span> Data fusion techniques in EO Data Cubes<br class="">
<span class="x_Apple-tab-span" style="white-space:pre"></span> Data mining using Machine Learning, Deep Learning,
<br class="">
<span class="x_Apple-tab-span" style="white-space:pre"></span> Data quality, reliability,
<br class="">
<span class="x_Apple-tab-span" style="white-space:pre"></span> Cost/Benefits analysis of EO Data Cubes<br class="">
<span class="x_Apple-tab-span" style="white-space:pre"></span> Thematic applications (e.g. biodiversity, climate, health, natural hazards,
) using EO Data Cubes<br class="">
<span class="x_Apple-tab-span" style="white-space:pre"></span> New innovative tools and solutions to work with EO Data Cubes<br class="">
<span class="x_Apple-tab-span" style="white-space:pre"></span> Use of high to very-high resolution EO data<br class="">
<span class="x_Apple-tab-span" style="white-space:pre"></span> Integration of in-situ observations<br class="">
<span class="x_Apple-tab-span" style="white-space:pre"></span> Local, national, regional implementations<br class="">
<span class="x_Apple-tab-span" style="white-space:pre"></span> Cloud-based computing<br class="">
<span class="x_Apple-tab-span" style="white-space:pre"></span> Architecture design of EO Data Cubes (HPC, Distributed Computing, Super Computers)<br class="">
<span class="x_Apple-tab-span" style="white-space:pre"></span> Capacity building and training<br class="">
<span class="x_Apple-tab-span" style="white-space:pre"></span> Support to policy framework such as the Sustainable Development Goals, the Paris agreement, Aichi targets, or Water Framework Directive<br class="">
<span class="x_Apple-tab-span" style="white-space:pre"></span> Links with initiatives like Copernicus or the Global Earth Observation System of Systems (GEOSS).<br class="">
<br class="">
Guest Editors:<br class="">
Dr. Gregory Giuliani (University of Geneva & GRID/Geneva)<br class="">
Dr. Brian Killough (NASA/CEOS)<br class="">
Dr. Stuart Minchin (Geoscience Australia)<br class="">
Prof. Dr. Gilberto Camara (Group on Earth Observations)<br class="">
<br class="">
Important dates:<br class="">
September 1, 2018: Launch of the Call for Papers<br class="">
December 31, 2018: Deadline for abstract submission (800-word) to guest editors<br class="">
January 15, 2018: Notification of abstract acceptance and full paper submission invitation<br class="">
April 30, 2019: Deadline for submission<br class="">
June 30, 2019: Revision/rejection notification<br class="">
October 31, 2019: Paper acceptance notification<br class="">
<br class="">
More information & submission: <a href="https://www.mdpi.com/journal/data/special_issues/EODC" class="OWAAutoLink" id="LPlnk840510" previewremoved="true">https://www.mdpi.com/journal/data/special_issues/EODC</a><br>
<br class="">
<br class="">
Do not hesitate to spread the word around you and share this announcement.<br class="">
<br class="">
Sorry for the cross-posting!<br class="">
<br class="">
On behalf of the guest editors,<br class="">
<br class="">
Gregory Giuliani<br class="">
<div class="">
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<div style="color:rgb(0,0,0); font-family:Helvetica; font-size:12px; font-style:normal; font-variant-caps:normal; font-weight:normal; letter-spacing:normal; text-align:start; text-indent:0px; text-transform:none; white-space:normal; word-spacing:0px">
Dr. Gregory Giuliani<br class="">
Lecturer in Earth Observations - UNIGE</div>
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Head of the Digital Earth Unit - GRID-Geneva<br class="">
http://www.unige.ch/envirospace/people/giuliani/<br class="">
**************************************************************<br class="">
University of Geneva<br class="">
Institute for Environmental Sciences/enviroSPACE<br class="">
66 Boulevard Carl-Vogt, CH - 1205 GENEVE<br class="">
Phone: (+41 22) 379 07 09 Fax: (+41 22) 379 07 44<br class="">
Web: http://www.unige.ch/envirospace<br class="">
Email: gregory.giuliani@unige.ch</div>
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**************************************************************<br class="">
UN Environment/Science Division/GRID-Geneva<br class="">
International Environment House<br class="">
11 chemin des Anιmones, CH - 1219 CHATELAINE<br class="">
Phone: (+41 22) 510 56 78 <br class="">
Web: http://www.grid.unep.ch<br class="">
Email: gregory.giuliani@unepgrid.ch<br class="">
**************************************************************</div>
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