A vast amount of Earth Observation (EO) data is produced daily and made available through online services and repositories. Contemporary and historical data can be retrieved and used to power existing applications, to foster innovation and finally improve the EU citizens’ lives. However, an undersized audience follows this activity, leaving huge volumes of valuable information unexploited. EO4EU aims to provide innovative tools, methodologies and approaches that would assist a wide spectrum of users, from domain experts and professionals to simple citizens to benefit from EO data. EO4EU strives to deliver dynamic data mapping and labelling based on AI adding FAIRness to the system and data. EO4EU introduces an ecosystem for holistic management of EO data, bridging the gap among domain experts and end users, and bringing in the foreground technological advances to address the market straightness towards a wider usage of EO data. EO4EU envisages to boost the Earth Observation data market, providing a digestible data information modeling for a wide range of EO data, through dynamic data annotation and a state-of-the-art serverless processing by leveraging important European Cloud & HPC infrastructures.
The role of our group within EO4EU is linked with the Machine Learning related tasks of the project, which can be summarized in two main directions:
- The study and application of Self-Supervised learning models which will help us exploit the vast volume of unlabelled EO data, in order to minimize the annotation effort required in downstream supervised tasks. In this way, new use cases will be enabled to efficiently train classifiers, with a significantly reduced required budget of annotated/ labelled data. This would facilitate the greater public and non-experts interested institutions and individual to access and efficiently use these data. To achieve this, we explore the latest advancements in Self-Supervised Contrastive Learning.
- The development of tailored models of Learned Compression, suitable to the EO ecosystem. Based on previous expertise in the development of models of Learned Compression, we will develop the models that will increase the compression efficiency comparing to standard, generic, off-the-shelf compression solutions. To do so, we will exploit the available EO data sources to tailor models to the corresponding data distribution. In this way, we will facilitate the efficient data transferring for the use cases that require it.
More information about the project can be found in the following links.
Project's website: eo4eu.eu