(a) Mosaic of level-1 ground range detected Sentinel-1 (A and B) products with dual polarisation (VV+VH or HH+HV), covering the entire globe and represented as a false colour RGB composition; (b) Large scale mosaics of satellite data are crucial for several applications that involve geo-analysis with remote sensing data such as mapping large natural hazard areas, landcover classification and for guiding field investigations.
- Pierre Soille
How to cite
Syrris, Vasileios; Corbane, Christina; Pesaresi, Martino; Soille, Pierre (2017): Mosaic of Copernicus Sentinel-1 Data at Global Scale. European Commission, Joint Research Centre (JRC) [Dataset] doi: 10.2905/jrc-bigdataeoss-s1-mosaic PID: http://data.europa.eu/89h/jrc-bigdataeoss-s1-mosaic
A TeraPixel global mosaic of Copernicus Sentinel-1 data at about 20m spatial resolution and covering most the land mass has been created on the JRC Earth Observation Data and Processing Platform (JEODPP).
The scope of this mosaic is to provide a global base layer for visual assessment of natural and man-made features such as built-up areas.
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS, Piscataway, UNITED STATES OF AMERICA
This paper presents a processing chain for handling big volume of remotely sensing data for generating wide extent mosaics. More specifically, the data under consideration are level-1 ground range detected Sentinel-1 products with dual polarisation (VV+VH or HH+HV). Two approaches for a) distribution discretization accompanied by false color composition and b) image rendering and mosaicking are proposed. While these two components are necessary constituents of the presented mosaicking workflow, they can operate independently of each other. The design of the processing chain satisfies three objectives: i) contrasting derivative products of the input Sentinel-1 imagery such as the Global Human Settlement Layer, ii) adapting on a high-throughput computing system for fast execution, and iii) allowing potential extensions to more complex applications such as the image classification. Fast processing, process automation, incremental adjustment and information distinction are the main advantages of the proposed method. Elaboration and focus on these features are carried out during the presentation of the results.
- Publications Office of the European Union, Luxembourg, Luxembourg
Big Data from Space refers to Earth and Space observation data collected by space-borne and ground- based sensors. Whether for Earth or Space observation, they qualify being called 'big data' given the sheer volume of sensed data (archived data reaching the exabyte scale), their high velocity (new data is acquired almost on a continuous basis and with an increasing rate), their variety (data is delivered by sensors acting over various frequencies of the electromagnetic spectrum in passive and active modes), as well as their veracity (sensed data is associated with uncertainty and accuracy measurements). Last but not least, the value of big data from space depends on our capacity to extract information and meaning from them.
The goal of the Big Data from Space conference is to bring together researchers, engineers, developers, and users in the area of Big Data from Space.
The 2017 "Big Data from Space" conference (BiDS'17) was held from the 28th to the 30th of November 2017 at the Pierre Baudis conference Centre (Toulouse, France).
BiDS'17 was co-organised by the European Space Agency (ESA), the Joint Research Center of the European Commission (JRC), and the European Union Satellite Centre (SatCen) and hosted by CNES, the French Government Space Agency.
These proceedings of the conference consist of a collection of 181 short papers corresponding to the oral and poster presentations presented at the conference. They provide a unique snapshot of the current research activities, developments, and initiatives in Big Data from Space.
|From date||To date|
- Published by
- European Commission, Joint Research Centre
- Created date
- Modified date
- Issued date
- Data theme(s)
- Environment, Science and technology
- Update frequency