“Data” Special Issue “Earth Observation Data Cubes”
Remotely sensed Earth Observations (EO) data have already exceeded the petabyte-scale and are increasingly freely and openly available from different data holdings. This poses a certain number of issues in terms of volume (e.g., data volumes have increased 10x in the last 5 years); velocity (e.g., Sentinel-2 is capturing a new image of any given place every 5 days); and variety (e.g., different types of sensors, spatial/spectral resolutions). Traditional approaches to the acquisition, management, distribution and analysis of EO data have limitations (e.g., data size, heterogeneity and complexity) that impede their true information potential to be realized.
The fact that the full information potential of EO data has not yet been realized and therefore remains still underutilized is explained by various reasons: (1) It requires scientific knowledge to understand what data is needed… optical (which resolution?), radar (which type?); (2) It is difficult to access and download the increasing volumes of data generated by satellites; (3) there is a lack of expertise and computing resources to efficiently prepare and utilize EO data; (4) the particular structure of EO data; and (5) the significant effort and cost required to store and process data limit its effective use.
Addressing Big Data challenges such as volume, velocity and variety, requires a change of paradigm and moving away from traditional local processing and data distribution methods to lower the barriers caused by data size and related complications in data management. In particular, data volume and velocity will continue to grow as the demands increase for decision-support information derived from these data.
To tackle these issues and bridge the gap between users’ expectations and current Big Data analytical capabilities, EO Data Cubes (EODC) are a new paradigm revolutionizing the way users can interact with EO data and a promising solution to store, organize, manage and analyze EO data. The main objective of EODC is to facilitate EO data usage by addressing volume, velocity, variety challenges and providing access to large spatio-temporal data in an analysis ready format.
Different EODC implementations are currently operational such as Digital Earth Australia, the Swiss Data Cube, the EarthServer, the E-sensing platform or the Google Earth Engine. These initiatives are paving the way to broaden the use of EO data to larger communities of users; support decision-makers with timely and actionable information converted into meaningful geophysical variables; and ultimately are unlocking the information power of EO data.
This Special Issue is consequently aiming to cover the most recent advances in EODC developments and implementations and welcomes contributions with respect to (but without being restricted to):
Methods for generating Analysis Ready Data for both optical and SAR imagery
Interoperability challenges between EO Data Cubes
Algorithms for generating decision-ready products
Data fusion techniques in EO Data Cubes
Data mining using Machine Learning, Deep Learning, …
Data quality, reliability, …
Cost/Benefits analysis of EO Data Cubes
Thematic applications (e.g. biodiversity, climate, health, natural hazards, …) using EO Data Cubes
New innovative tools and solutions to work with EO Data Cubes
Use of high to very-high resolution EO data
Integration of in-situ observations
Local, national, regional implementations
Cloud-based computing
Architecture design of EO Data Cubes (HPC, Distributed Computing, Super Computers)
Capacity building and training
Support to policy framework such as the Sustainable Development Goals, the Paris agreement, Aichi targets, or Water Framework Directive
Links with initiatives like Copernicus or the Global Earth Observation System of Systems (GEOSS).
The fact that the full information potential of EO data has not yet been realized and therefore remains still underutilized is explained by various reasons: (1) It requires scientific knowledge to understand what data is needed… optical (which resolution?), radar (which type?); (2) It is difficult to access and download the increasing volumes of data generated by satellites; (3) there is a lack of expertise and computing resources to efficiently prepare and utilize EO data; (4) the particular structure of EO data; and (5) the significant effort and cost required to store and process data limit its effective use.
Addressing Big Data challenges such as volume, velocity and variety, requires a change of paradigm and moving away from traditional local processing and data distribution methods to lower the barriers caused by data size and related complications in data management. In particular, data volume and velocity will continue to grow as the demands increase for decision-support information derived from these data.
To tackle these issues and bridge the gap between users’ expectations and current Big Data analytical capabilities, EO Data Cubes (EODC) are a new paradigm revolutionizing the way users can interact with EO data and a promising solution to store, organize, manage and analyze EO data. The main objective of EODC is to facilitate EO data usage by addressing volume, velocity, variety challenges and providing access to large spatio-temporal data in an analysis ready format.
Different EODC implementations are currently operational such as Digital Earth Australia, the Swiss Data Cube, the EarthServer, the E-sensing platform or the Google Earth Engine. These initiatives are paving the way to broaden the use of EO data to larger communities of users; support decision-makers with timely and actionable information converted into meaningful geophysical variables; and ultimately are unlocking the information power of EO data.
This Special Issue is consequently aiming to cover the most recent advances in EODC developments and implementations and welcomes contributions with respect to (but without being restricted to):
Methods for generating Analysis Ready Data for both optical and SAR imagery
Interoperability challenges between EO Data Cubes
Algorithms for generating decision-ready products
Data fusion techniques in EO Data Cubes
Data mining using Machine Learning, Deep Learning, …
Data quality, reliability, …
Cost/Benefits analysis of EO Data Cubes
Thematic applications (e.g. biodiversity, climate, health, natural hazards, …) using EO Data Cubes
New innovative tools and solutions to work with EO Data Cubes
Use of high to very-high resolution EO data
Integration of in-situ observations
Local, national, regional implementations
Cloud-based computing
Architecture design of EO Data Cubes (HPC, Distributed Computing, Super Computers)
Capacity building and training
Support to policy framework such as the Sustainable Development Goals, the Paris agreement, Aichi targets, or Water Framework Directive
Links with initiatives like Copernicus or the Global Earth Observation System of Systems (GEOSS).
MDPI Special Issue: Earth Observation Data Cubes