Webinar: BETTER at the EO Big Data Hackathon 2019

This Webinar was primarily intended to prepare the participants in the 2019 EO Big Data Hackathon with the material and exercises expected in the BETTER session at the event. Nevertheless, it contains insights into the results already achieved, and those planned in the rest of the project.


Join us at the EO Joint Big Data Hackathon: Frascati, Italy, 7-8 November 2019

The BETTER project is happy to invite all EO practitioners to join the H2020 EO Big Data Hackathon, a event that is being jointly co-organised by five EU-funded Horizon 2020 projects. Register now for free and join us in Frascati!


Exploiting German Space Agency interferograms to investigate the effects of Co-Seismic deformation

In the second Geohazards challenge BETTER exploits Sentinel-1 interferograms generated systematically by the German Space Agency (DLR) to study co-seismic deformation effects, such as amplitude and extension of the deforming area and identification of fault traces. Apart from offering visual data mining, results can calibrate machine learning algorithms that can help evaluating seismic hazard.


Global catalogue of co-seismic deformation (2002-2010)

The first challenge considered for the Geospatial Intelligence domain investigates whether the relationship between fault offset and seismic-wave induced shaking may provide a better understanding of earthquake hazard.


Illicit Crop Monitoring based on Sentinel-2 data

The third challenge identified for the Geospatial Intelligence domain focuses on the analysis of multispectral satellite image time series to offer remote sensing as an alternative to on-field surveying to detect illicit crop production.


Change Detection based on SAR Single Look Complex (SLC) data

The second challenge being explored in the Geospatial Intelligence category is the use of Single Look Complex StripMap Sentinel-1 images for the monitoring of an area of interest to detect natural or human activities.

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Analyzing Water and Vegetation indices based on Sentinel-2 data

The first challenge identified for the Geospatial Intelligence domain investigates fast and intuitive means to derive information from satellite data on specific phenomena, through multi-temporal analysis of thematic indices. This challenge focuses on water and vegetation indices.


BETTER supports WFP Challenges for precise Humanitarian Assistance

BETTER is supporting WFP to optimize its humanitarian intervention plans, which are not only related to emergency or crisis responses but include interventions that aim to enhance the resilience of communities and the increase in community assets.

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BETTER is devoted to maximizing the exploitation of Copernicus to address Key Societal Challenges

Whether you are an EO service provider, a non-EO expert decision maker or a developer, BETTER can boost your EO solutions with the advantages of Big Data technologies. If you are interested in becoming a new Challenge Promoter in 2019 or you would like to assess other possibilities to engage with BETTER, contact us info@ec-better.eu.

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Big Data.. from Space!

The BETTER project will be identifying up to 36 big data challenges over the course of 3 years, starting with the 9 identified for 2018. Strictly viewed from a data ‘volume’ perspective, some of the identified challenges do not at first glance appear to handle ‘big’ data. On closer inspection however, they nevertheless pose a big data problem. In this blogpost we explain why.

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A BETTER project for exploiting Big Data in Earth Observation

Big-data Earth observation Technology and Tools Enhancing Research and development is an EU-H2020 research and innovation project, started in November 2017 to the end of October 2020.

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SANSA 0.4 (Semantic Analytics Stack) Released

The SANSA Stack is one of the earmarked big data analytics components tsansa-stack-architecture.pngo be employed in the BETTER data pipelines. The Smart Data Analytics group announced yesterday SANSA 0.4 - the fourth release of the Scalable Semantic Analytics Stack.
SANSA employs distributed computing via Apache Spark and Flink in order to allow scalable machine learning, inference and querying capabilities for large knowledge graphs.

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