- Computting & Cloud
- Data & Analytics
- What We Offer
- Your Processing Environment
- Example of tool usage
- Processing EO Data and Serving www services
- Processing and Storing EO
- Embedding OGC WMS Services into Your website
- GPU Use Case
- Using the EO Browser
- EO Data Finder API Manual
- Use of SNAP and QGIS on a CREODIAS Virtual Machine
- Use of WMS Configurator
- DNS as a Service - user documentation
- Use of Sinergise Sentinel Hub on the CREODIAS EO Data Hub
- Load Balancer as a Service
- Jupyter Hub
- Use of CREODIAS Finder for ordering data
- Legal Matters
- Pricing Plans
- Public Reporting Dashboards
- Sentinel Hub Documentation
- Integration Guides
- OGC API
- Custom Processing Scripts
- Partner Services
Your Processing Environment
Use of Sinergise Sentinel Hub on the CREODIAS EO Data Hub
The Sentinel Hub application, developed by Sinergise is installed on CREODIAS. With Sentinel Hub, large amounts of Sentinel, Landsat, MODIS and other satellite data can be accessed in a very fast and user-friendly way through their Playground and EOBrowser (https://www.sentinel-hub.com/explore/sentinel-playground, https://www.sentinel-hub.com/explore/eobrowser). However, the main strength of Sentinel Hub is to access data via the OGC standard. This standard can be used to display satellite data as web maps (through WMS protocols) as web tiles (WTS) or coverage (WCS). The first two protocols are primarily useful to display satellite data as background maps in GIS or websites. The WCS protocol allows further processing and analysis of satellite data.
This use case shows how Sentinel Hub can be used to process and analyse satellite data from the CREODIAS EO Data Hub.
Sinergise has developed eo-learn, a Python-based set of libraries, as a data processing and analysis extension to facilitate the use of Sentinel Hub. These libraries comprise a wide range of satellite data processing and analysis tools with which the user can carry out a satellite data analysis from beginning to end. The library is well documented and includes a nuber of examples in Jupyter Notebook format, which can be run from any computer (https://eo-learn.readthedocs.io/en/latest/index.html). In this use case we will run a Jupyter Notebook example to generate a Sentinel-2 time-lapse image.
Running eo-learn on EO Data Hub
In order to run eo-learn on CREODIAS EO Data Hub, users need to be registered on the CREODIAS platform. After registration, an application for EO Data Hub access needs to be made and OGC settings must be configurated with the WMS configurator. More information on the EO Data Hub access and the WMS configurator here: (https://CREODIAS.eu/use-of-wms-configurator). After a configuration has been set, data stored on EO Data Hub can be accessed through OGC access (WMS, WMTS, WFS or WCS) and be used in a number of applications, like QGIS, Google Earth or custom scripting. In this example, an existing eo-learn Python Jupyter Notebook is adapted so data stored on EO Cloud is used.
Screenshot of WMS Configurator
Example use cases
The eo-learn libraries can be used in a number of ways. Sinergise has developed a number of example Jupyter Notebooks to familiarise users with the capabilities. In this example we adapted a Jupyter Notebook that searches Sentinel-2 data of a specified area over a specified period, in this case Poland’s biggest lake Śniardwy between April 2018 and March 2019. In order to achieve this, a number of eo-learn tools are used in a workflow, namely data search, cloud cover definition and filtering images by cloud cover presence. This is a fairly simple workflow, the eo-learn library has a number of more advanced tools. A great example is provided by the Bluedot Obervatory platform, which utilises Sinergise eo-learn tools for lake water level monitoring (https://www.blue-dot-observatory.com/).
Sentinel-2 image of ice-covered Śniardwy lake on 24th of February 2019
The Sinergise eo-learn libraries combined with data access through EO Data Hub offers an opportunity to use advanced Earth Observation data analysis tools through the CREODIAS platform directly. Through this, advanced EO data processing and analysis can be carried out on the CREODIAS platform without the need for subscription to other cloud processing services.