Proceedings
RUS: A New Expert Service for Sentinel Users †
Francesco Palazzo 1,*, Tereza Šmejkalová 1, Miguel Castro-Gomez 1, Sylvie Rémondière 1,
Barbara Scarda 1, Béatrice Bonneval 1, Chloé Gilles 1, Eric Guzzonato 2 and Brice Mora 2
Serco SPA, Frascati 00044, Italy; tereza.smejkalova@serco.com (T.Š.);
miguel.castro.gomez@serco.com (M.C.-G.); sylvie.remondiere@serco.com (S.R.);
barbara.scarda@serco.com (B.S.); beatrice.bonneval@serco.com (B.B.);
chloe.gilles@serco.com (C.G.)
2 CS-SI, 92350 Le Plessis Robinson, France; eric.guzzonato@c-s.fr (E.G.); brice.mora@c-s.fr (B.M.)
* Correspondence: francesco.palazzo@serco.com; Tel.: +39-069-419-0682
† Presented at the 2nd International Electronic Conference on Remote Sensing, 22 March–5 April 2018;
Available online: https://sciforum.net/conference/ecrs-2.
1
Published: 23 March 2018
Abstract: With large volumes of data acquired every month, the Copernicus satellites provide
essential information for analysing and monitoring our environment. However, technical and
knowledge barriers may affect user’s uptake of such a wealth of information. The RUS (Research
and User Support for Sentinel Core Products) Service (funded by the EC and managed by ESA)
began operations in October 2017 and aims to support overcoming such issues. A scalable cloud
environment offers the possibility to remotely store and process data by bringing data and
associated processing closer to the user. An integral part of the solution is the exploitation and
adaptation of the platform, Free and Open-Source Software (FOSS). In addition, technical and
scientific support (including training sessions) are provided to simplify exploitation of Copernicus
data. The RUS Service is specially addressed to users from Copernicus countries who are willing to
discover and use Copernicus core products and datasets. Other users willing to access the Service
should first liaise with RUS to check their eligibility. The service is free. Commercial and
operational activities cannot be carried out through the RUS Service.
Keywords: Copernicus; virtual machines; training; earth observation applications
1. Introduction
In November 2016 the monthly volume of data acquired by the three operational Copernicus
satellites (Sentinel-1a, Sentinel-1b and Sentinel-2a) accounted for 150 TB [1]. With the launch of
Sentinel-2b, Sentinel-3a and Sentinel-5P this volume of data has at least tripled, implying that with a
download speed of 15 Mps (average connection speed in Europe [2]), almost 8 years would be
needed to download one month of all observations. In addition, a very performant computing
power is needed to process such data (whose size, in the case of Sentinel-1 might be larger than 1
GB/product and 500 MB/product for Sentinel-2). Finally, besides “physical barriers” there might be
“knowledge barriers” related to the complexity of the image information, understanding of the
formats, the applicability of the data to specific applications or reluctance to absorb the data into
pre-existing routines managed by the user.
RUS (Research and User Support for Sentinel Core Products) was launched with the purpose of
helping to overcome these problems. The service is offered at no cost and addresses the needs (in
terms of technical and scientific support, computing resources and disk space identified by ESA) of
different types of users (basic users in need of downloading support; R&D users in need of
prototyping support and proficient users, in need of processing support).
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2. Methods
The following paragraph provides an overview of the methods and solutions put in place by
the RUS consortium to mitigate the problems faced by Copernicus users.
2.1. ICT Solutions
To overcome physical issues (e.g., downloading, storing and processing), the service exploits
Infrastructure as a Service (IaaS) and Platform as a Service (PaaS). IaaS includes network access,
Virtual Machines (VM) with Computing Processing Units (CPU) and a scalable storage capacity. The
PaaS includes data access (direct access to Copernicus Hub), communication tools (mail, chat,
audio-conference and video-conference with the Helpdesk), processing and viewing tools,
development tools, collaboration tools, as well as all necessary and relevant documentation and
internet links. FOSS is pre-installed on demand on the VM, however users are free to install their
Commercial Off-The-Shelf (COTS) software on the machine.
The infrastructure relies on several types of virtual environments:
•
•
Collaboration environments hosting a platform to offer collaboration services such as
video-conference and chat, the Front Desk, the Administration Desk and the Service
Management Desk.
User environments hosting the development and processing platform: each RUS user could
have access to a dedicated cluster of user environments.
Thanks to this environment, RUS Users can access Sentinel data using the data platform,
develop algorithms and process this data using their dedicated cluster and benefit from interactive
support from RUS Operators through services offered by the collaboration platform.
Use of Copernicus datasets as the main source of information is a prerequisite to access the RUS
Service, but non-Copernicus data (EO and other data) can also be freely used and imported by the
users. The VMs provided by RUS work on a Linux environment where either FOS or COTS can be
installed and also includes programming and scripting environments. Default Processing libraries
account for: GDAL, Sentinel Toolboxes, Orfeo Toolbox and SNAPHU; pre-installed processing tools
include QGIS and SNAP, whereas current software development utilities are: Oracle JDK 1.8,
Apache Ignite, Eclipse, GCC, CMAKE, Maven, GIT, Python 2.7/3.5, and R 3.3. The ICT for the user is
defined following an analysis of the received service request; such analysis defines the scaling of the
work environment in terms of duration, disk space and size (number of Virtual Machines, number of
cores per machine, RAM per core).
Considering resource constraints, the RUS Service can be offered to each user for a limited
amount of time and including ICT/Expert/Data resources compatible with declared uptake
objectives and current user demand. Three pre-defined work environments are typically proposed:
1–4 cores with disk space up to 1 TB for 3 months, 1–10 cores with disk space up to 10 TB for 6
months or up to 40 cores with disk space up to 50 TB for 6 months.
More information about the RUS Service and access to the VM can be found at:
https://rus-copernicus.eu/portal/.
2.2. Building Knowledge
To complement the ICT offer, training and outreach activities aiming to create a critical mass of
Copernicus data users and focusing on a large portfolio of applications, are side supporting activities
surrounding the service pyramidal layers. Use of the RUS Virtual Machines with pre-installed FOSS
facilitates handling of such events, where participants can use their own laptops to manage the
processing. The use of the same configuration for each VM in fact discards any pre-existing
difference between the used laptops (and operating systems), facilitating the smooth running of the
event. Face to face events are organized to meet the requirements of small groups of users which
receive specific training on EO theory and then are guided step-by-step by the trainers, in the
application of the learned theory in practical case studies. The assigned VM remains accessible to the
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user for several months after the training, so as to allow repeating or completing the exercises (or
performing other processing activities).
Large Webinars organized every month aim to attract new potential users by providing in a
condensed format, the instructions to perform some basic processing steps to exploit Sentinel data
for a specific application. They are closed by Q&A sessions, offering the participants the possibility
of interacting with the trainer. The Webinars are recorded and made publically available for re-play
on a dedicated YouTube channel. Users interested in repeating the exercise can either use FOSS
installed on their computer or ask RUS for access to the pre-configured VM with all the material
needed to perform the exercise.
The theory lectures given during the face to face events are recorded and assembled with
questions and multiple-choice answers and are made available on an E-learning portal. Scores are
assigned for each completed course and badges are given to the users.
More information and access to RUS Training Resources can be found at:
https://rus-training.eu/
3. Results and Discussion
In this paragraph we provide a few examples of processing results focusing on different
applications, obtained by exploiting the service to prepare training sessions. The data and software
needed to re-play the exercises are freely available within the RUS environment, together with the
step-by-step instructions to generate most of the presented results.
3.1. Ship Detection
Ship detection with Sentinel-1 enables detection of vessels not carrying an Automatic
Identification System (AIS) or other tracking systems on board, such as smaller fishing ships or ships
that might be in the surveyed area illegally (illegal fishing, piracy etc.). As SAR is not reliant on solar
illumination and is rather independent of weather conditions, frequent monitoring is possible. The
exercise exploits ESA’s Open Source Sentinel-1 Toolbox to process Sentinel-1 data, detecting targets
larger than 30 m in the Gulf of Trieste. Final output can be visualized in Google Earth (Figure 1) or be
exported as a point layer to an Open source GIS (QGIS). RUS VM are used for running the exercise.
More information about the use of Sentinel-1 data for maritime surveillance can be found in [3].
Figure 1. Ship detection in the gulf of Trieste. Sentinel-1 products can be easily used for ship
monitoring. In this case a single Sentinel-1 product was used and the kml derived from the analysis is
shown on Google Earth. Each detected target is associated to information about estimated target length.
3.2. Burned Area Mapping
Two Sentinel-2 products acquired before and after a series of wildfires which affected central
Portugal in June 2017 are used to map the location and intensity of damage (burn severity). The
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exercise exploits ESA’s Open Source Sentinel-2 Toolbox to process Sentinel-2 data, comparing preand post-event imagery and calculating the Relativized Burn Ratio (RBR) [4]. Processed results are
then exported to an Open source GIS (QGIS)—Figure 2, where post-processing (classification of
severity level, following USGS suggested classification) is performed. RUS VM are used for running
the exercise.
Figure 2. Burned area detection in Portugal. Two Sentinel-2 products acquired before and after the
wildfires of 17–18 June 2017 are used to locate the area affected by the fires and assess burned
severity. The image shows the output map visualized with QGIS (installed on the RUS VM).
3.3. Deformation Monitoring
Two Sentinel-1 images acquired before and after the Iran earthquake of 12 November 2017 are
used to create the deformation map associated to the event. In this case ESA’s Open Source
Sentinel-1 Toolbox is used to create an interferogram from a couple of ascending acquisitions and to
derive line of sight subsidence/uplift associated with the event (Figure 3). RUS VM was used for
processing.
Figure 3. Use of RUS for Earthquake studies. (a) Fringes computed from 2 ascending InSAR pairs
acquired by Sentinel-1 before and after the Iran earthquake of 12 November 2017; (b) Deformation
field (along satellite’s line of sight) extracted from the observations.
3.4. Discussion
In the first two cases presented, lack of in situ observations simultaneous to the acquisition do
not allow thorough validation of the results, therefore they should only be considered as examples of
well-established methodologies. In the case of the deformations associated to the November
earthquake, the distribution of fringes and estimated line of sight motion are well in agreement with
studies carried out by other authors with the same datasets, as well as with different data [5].
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Tutorials to reproduce the results described above for exploiting the RUS service are being
made
freely
available
on
the
dedicated
RUS
YouTube
channel
https://www.youtube.com/channel/UCB01WjameYMvL7-XfI8vRIA.
Furthermore
upcoming
training events are announced through social media, such as Twitter (@RUS_Copernicus) and
Facebook (https://www.facebook.com/RUS-Copernicus-1940884026129145).
4. Conclusions
The RUS Service is a new, free service carried out by an international team, led by C-S France
and involving Serco SPA, Noveltis, Along-Track and C-S Romania. The main aim of RUS is to
promote uptake of Copernicus satellite data. This is achieved by facilitating user access and
exploitation of the data, through the use of VMs with associated processing power, and by carrying
out training and education activities.
Author Contributions: Eric Guzzonato and Brice Mora provided the information about ICT; Barbara Scarda,
Chloé Gilles and Béatrice Bonneval are contributing to the development of the E-learning portal; Tereza
Šmejkalová and Miguel Castro-Gomez developed the exercises and provided the inputs for paragraph 4;
Francesco Palazzo and Sylvie Rémondière wrote the paper.
Acknowledgments: The RUS Service is funded by the European Commission and managed by ESA (contract
4000119093/17/I-LG).
Conflicts of Interest: The authors declare no conflict of interest. The founding sponsors had no role in the
design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript, and in
the decision to publish the results.
Abbreviations
The following abbreviations are used in this manuscript:
RUS
EC
ESA
FOSS
TB
Mps
GB
MB
R&D
ICT
IaaS
PaaS
VM
CPU
COTS
DHuS
EO
GDAL
SNAPHU
QGIS
ESAMDPI
SNAP
JDK
GCC
RAM
Q&A
HW
SW
AIS
Research and User Support
European Commission
European Space Agency
Free and Open-Source Software
Terabyte
Megabit per second
Gigabyte
Megabyte
Research & Development
Information and Communications Technology
Infrastructure as a Service
Platform as a Service
Virtual Machine
Computing Processing Unit
Commercial Off The Shelf
Data Hub Service
Earth Observation
Geospatial Data Abstraction Library
Statistical-Cost, Network-Flow Algorithm for Phase Unwrapping
Quantum Geographic Information System
Multidisciplinary Digital Publishing Institute
Sentinel Application Platform
Java™ Standard Edition Development Kit
GNU Compiler Collection
Random access Memory
Questions and Answers
Hardware
Software
Automatic Identification System
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SAR
GIS
RBR
USGS
LOS
InSAR
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Synthetic Aperture Radar
Geographic Information System
Relativized Burned Ratio
United States Geological Survey
Line of sight
Interferometric Synthetic-Aperture Radar
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© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).