Papers

Remote sensing is a technique that allows obtaining information about an object through the analysis of data acquired by sensors that are not in contact with it, and the detection of changes from a multi-temporal sequence of satellite data is one of its most important applications.

As is well known, this process requires the application of atmospheric corrections to the images, so that the changes detected are only attributable to real landscape modifications. One of the techniques often recommended for applications of classification and detection of changes is the Dark Object Subtraction (DOS).

The technological advances in the sensors and the spatial and temporal resolutions now available open the door to new ways of monitoring the Earth by applying the latest techniques in machine learning or deep learning to these data.

EGU ( European Geosciences Union) 2019

Vienna | Austria | 7–12 April 2019

2019/04 - Deep Learning as a Service for Satellite Imagery Super-resolution

Deep Learning as a Service for Satellite Imagery Super-resolution

The problem of performing we try to solve is what we call multi-spectral imagery super-resolution. Satellites usually take images at different light frequencies and these different bands usually have different resolutions. For example in the case of ESA’s Sentinel-2 satellite it has 4, 6 and 3 bands with 10m, 20m and 60m resolutions respectively.

The idea is to take these low resolution bands (20m and 60m) and to super-resolve them to high resolution (10m). Several methods have been implemented to do this, but we choose to follow [1] that is based on deep neural networks and has shown to outperform previous state-of-the-art pansharpening methods. The results of using this method can be seen in the figure below:

We have modified the original code of [1] in order to make it general enough to be trained on any satellite. In fact we plan to launch services to perform super-resolution on other satellites like LandSat 8 in addition to the current service for Sentinel 2.

EOSC-hub week 2019

10 - 12 April , Prague

2013/10 - Experience with a frozen computational framework from LEP age

Experience with a frozen computational framework from LEP age

By the end of the LEP era there was no well established strategy for the long term preservation of the physics results and data processing framework.

This happening at a time before the generalization of the virtualization techniques, several alternatives were studied.
Among them the setup of a dedicated computational cluster to be preserved medium-long tem in its original state that would provide the capability to reanalyse LEP experiments’ data in case the LHC results, and in particular the Higgs boson search, indicated that was required.
In 2001 we implemented such strategy for the DELPHI experiment at the Institute of Physics of Cantabria, IFCA, placed in Santander (SPAIN).

CHEP 2013

14 - 18 October , Amsterdam

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Papers

2017 - Improved Cloud resource allocation: how INDIGO-DataCloud is overcoming the current limitations in Cloud schedulers

2016 - Evolution of the open cloud computing interface

2014 - The role of IBERGRID in the Federated Cloud of EGI

2013 - Identity Federation with VOMS in Cloud Infrastructures

2013 - Analysis of Scientific Cloud Computing requirements

2012 - Production change management using Puppet, Git, Jenkins and Gerrit

2012 - IberCloud: federated access to virtualized resources

2011 - Software Provision Process for EGI

2011 - Fostering multi-scientific usage in the Iberian production infrastructure

2011 - Focusing on an integrated computing infrastructure: the IFCA experience

2011 - Experience on running the Ibergrid infrastructure within EGI

2010 - Virtualization and network mirroring to deliver High Availability to Grid services

2010 - Provisioning of Grid Middleware for EGI in the framework of EGI-InSPIRE

2010 - Int.eu.grid

2010 - Ibergrid transition to EGI

Presentations

Plantilla - 3

Posters

2019/04 - Deep Learning techniques for Satellite data: Atmospheric Corrections

2019/04 - Deep Learning as a Service for Satellite Imagery Super-resolution

2013/10 - Experience with a frozen computational framework from LEP age