Data is the new oil of the 21st Century: an immensely, untapped valuable asset.
We are living in a digital economy where data is more valuable than ever. It’s the key to the smooth functionality of everything from the government to local companies. Without it, progress would halt.

Big data is no longer a trend or a “nice to have in portfolio”, but rather a distinctive capability that is understanding and generating insights through digital services. Value is created by acquiring data, combining data from different sources, and providing access to it with low latency while ensuring integrity and rich user interfaces.

The powerful data techniques and tools allow collecting, analysing, processing, and visualising vast amounts of data. Open data initiatives gained momentum, and it provided broad access to huge amounts of data, and not only from satellites.

Our in-house developed technologies for big data processing put the user, the client and the ordinary people into a privileged position - which is having access to information long time allowed only to few. 

 

 

 

 


 

Big Data Analytics approach address various sectors, from Earth Observation applications to “life on Earth” services. Benefits from Big Data Analytics approach:

  • agility - algorithms and analytic tools ready to use in various sectors - EO services and different economic activities;
  • data source integration - integration of high volumes of heterogeneous data from miscellaneous sources;
  • advanced and predictive techniques - the tools and technologies allow for deep data analysis, advanced visualisation of data and analytics for augmented user experience;
  • costs reductions, time and resources optimization - information revealed based on EO data are cost efficient alternatives to ground displacements.

Our validated technologies are developed based on tools for “deep data analysis”:

Satellite Image Time Series (SITS)
EO monitoring services rely on the SITS technologies: we process data from geographic information systems, combined with temporal data series, which were not imaginable until recently, and transform the information into insights about the Earth’s dynamics.

Large amounts of data are being collected by satellites and the future increased datasets will need more advanced data visualisations and analytics capabilities.
Our big data mining technologies endorse the Earth Observation monitoring services development. A lot of connections are revealed and many of the human activities are better understood when approached from this perspective, like in the case of South America project.

 

 

 

 


 

Content Based Image Retrieval (CBIR)
If you search for vessel containers, oilfield, storage tanks, just to mention a few examples, CBIR technology can provide you with answers. CBIR is a process framework for efficiently retrieving images from a collection by similarity, based on patch search.

The query relies on extracting the appropriate characteristics quantities describing the desired contents of images, while automatically indexed based on their characteristics, such as colour, texture or shape. Active learning is an opportunity afforded by the machine learning algorithms, according to positive and negative examples.
See the technology at work in the open source platform OSIRIDE - Geospatial Agility.

Visual Data Mining (VDM)
VDM allows interactive data presentation and integrate advanced visualisation of data and analytics for augmented user experience. The tools allow for interactively and efficiently browsing and understanding of the structure of large data sets of EO imaging products.
Based on VDM maps, charts and diagrams, you can make better and faster a priori estimations on the feasibility of the desired image processing, by means of 3D visualisation. More details in our contribution to NASA World Wind.

Suitability Cover Engine (SUCE)
SUCE platform provide searches for valid pixel data (cloud free) based on its metadata, in order to retrieve optimal EO image products sets.
The query effectively returns EO products from identified Payload Data Ground Segment (PDGS), based on advanced user criteria and analytic needs, avoiding both manual filtering and transfer of useless data. Find out more about its use cases.

 Big Data Analytics approach provide solutions for major challenges in everyday life, from climate change and environmental monitoring to risk assessment and new economic and societal business opportunities.

Stay up to date with our technologies at work.