- What is CREODIAS?
- Computing & Cloud
- Data & Processing
- Pricing Plans
- Fight with COVID-19
- Examples of usage
- Processing Sentinel-5P data using HARP and Python
- EO Data Access (R)evolution
- Land cover classification using remote sensing and AI/ML technology
- AI-based satellite image enhancer and mosaicking tools
- Monitoring air pollution using Sentinel-5P data
- Species classification of forests
- Enabling AI / ML workflows with CREODIAS vGPUs
- Satellite remote sensing analyses of the forest
- Satellite-based Urban Heat Island Mapping on CREODIAS
- Old but gold - historical EO data immediately available and widely used on CREODIAS
- CREODIAS for emergency fire management
- AgroTech project as an example of how CREODIAS can be used for food and environmental research
- Monitoring Air Quality of Germany in Pre vs During COVID Lockdown Period
- EO4UA
- Common Agricultural Policy monitoring with Earth Observation
- Applications of CREODIAS data
- Meteorological data usage on the CREODIAS platform
- Building added value under Horizon Europe with CREODIAS
- CREODIAS: Introduction to SAR Sentinel-1 data
- Land subsidence and landslides monitoring based on satellite data
- Satellite imagery in support of the Common Agriculture Policy (CAP) and crop statistics
- Useful tools for data processing, available on CREODIAS platform
- CREODIAS for hydrological drought modelling
- CREODIAS for managing Urban Heat Islands
- CREODIAS for Digitising Green Spaces
- CREODIAS for Air Quality
- Advanced data processors on CREODIAS
- CREODIAS for your application
- Solutions for agriculture with CREODIAS
- Earth Observation data for Emergency response
- Security Applications with Satellite Data
- Climate Monitoring with Satellite Data
- Water Analysis on CREODIAS
- CREODIAS for land and agriculture monitoring
- Solutions for atmospheric analysis
- 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
- ESRI ArcGIS on CREODIAS
- Use of CEMS data through CREODIAS
- Searching, processing and analysis of Sentinel-5P data on CREODIAS
- ASAR data available on CREODIAS
- Satellite remote sensing analyses of the forest
- EO Data Catalogue API Manual
- Public Reporting Dashboards
- Sentinel Hub Documentation
- Legal Matters
- FAQ
- News
- Partner Services
- About Us
- Forum
- Knowledgebase
Example of usage
CREODIAS for your application
Cloud computing services with an EO data repository like CREODIAS is a perfect place for developing and implementing any environmental applications, models or tools that you can think of. It is only up to the user how our resources will be utilised. Depending on the needs, the system is scalable and meets precise requirements. SOLUM and Forest Digital Earth Precursor are two examples of solutions CloudFerro have recently been working on. The both show how CREODIAS can be applied according to the needs and expectations of our society and public administration.
SOLUM application on CloudFerro cloud
SOLUM (latin: only, earth, base, ground, foundation, simply, soil), is the name of the result application from a pilot project, carried out on request of the Polish Space Agency. The application uses Earth Observation data to support the process of assessing and verifying soil classification methods in Poland.
CloudFerro, together with the Geomatic Michał Wyczałek-Jagiełło company, has recently carried out this ambitious project aiming at assessing soil fertility on the basis of satellite data. Mapping this phenomena is crucial for public administration and government agencies.
Three Polish provinces were analysed that represent different agricultural land use specifications and environmental conditions. As a result of the research, a new Soil Fertility Potential (SFP) index was proposed, based on Sentinel-2 imagery. The index, together with a rich database of administrative layers, has become the pilot environmental application on CREODIAS. Uniformity, constant repetition and availability of Sentinel data together with computing powerful resources offered by CloudFerro, make this index simple to scale for whole Poland or Europe in future.
For the sake of the analysis, Sentinel-2 imagery taken outside the growing season e.g. like this placed below - in March was taken into account, that is before farmers started their husbandry activities. Bare soil on the True Colour satellite image is reflected as heterogenous brownish parcels.
Multitemporal set of Sentinel data allowed to detect parcels with bare soil and conduct spectral and spatial analysis in order to find relation to soil fertility.
SFP (Soil Fertility Potential) is just an example of successful implementation of Copernicus open data into a useful and operational product, which is key for the management of agricultural lands.
CREODIAS, as a reliable source of ready-to-use data available locally in a cloud computing environment, is an effective tool for any environmental analysis. Soils are just one example. Calculation of vegetation indices, finding corelation between ground and satellite data and integrating it with external data in various resolutions and formats – these can all be done in a faster and more efficient manner with CREODIAS.
The image below presents the SOLUM interface with Soil Fertility Potential (SFP) index calculated on Sentinel-2, in comparison with the orthophotomap of Puławy (Kurów) region.
FOREST DTEP – example of excellent CREODIAS utility
CloudFerro also contributes to the Forest Digital Twin Earth Procursor (FDTEP) ESA environmental project. The company has responded with its cloud resources and expertise to the challenge of modelling global forests in order to digitize future scenarios depending on climate change various directions and rates. CloudFerro has recently proved in tests that it can provide over 2PB data daily. That means the system can be scaled up and used for all other aspects of Digital Twin Earth Project, apart from forests.
SCALING APPLICATION POTENTIAL
CREODIAS users do not need to make investments, build infrastructure or download data to external processing platforms. They can develop applications with local data access and scale the data to benefit from:
- widening in terms of space – from local to regional one
- extending time range – from one event to trend analysis
- applying model to another data – extending its utilities
- using more type of input data – API, WMS/WMTS, NFS, S3
- implementing more model settings in parallel
- saving money, time and energy
DATA READY FOR ANALYSIS
CREODIAS provides its user with an online, local, immediate access to a vast amount of satellite data in various levels of processing, including PGaaS – Product Generation as a Service. In order to complement recipients with more professional products, a number of processors are available to automatically generate Analysis-Ready Data from Sentinel-1:
- Sentinel-1 CARD-BS terrain-corrected backscatter,
- Sentinel-1 CARD-COH6 interferometric coherence,
- Sentinel-1 COG Cloud-Optimised Geotiff,
and Sentinel-2:
- Sentinel-2 sen2cor L2A surface reflectance with sen2cor processor.
What is more important, additional processors can be delivered by the user in a form of a docker image which can be implemented on our platform. That kind of dockerized applications allows for minimizing the processing effort with the user’s own workflow rules.
UNIQUE AND UNIVERSAL QUALITY
There are several CREODIAS market advantages like experience, elasticity, scalability, effectiveness or reliability. But a real added value comes from smart and conscious federation of those attributes. Cloud computing and EO data repository complexity and synergy build great potential for powerful cutting-age applications not only for environment analysis.
Author: Sylwia Nasiłowska (PhD), Project Manager, CloudFerro