The overall goal of the ML4EO (Machine Learning for Earth Observation) project is to address the lack of adoption of Cloud and ML technologies by proposing a software stack aimed at simplifying the access to this technologies. As with many technologies the adoption of novel ML methods and tools brings with it a steep learning curve for the end-user. This learning curve may hinder the adoption of such solutions. Specialized ML platforms like Theano, Caffe, TensorFlow have the potential to perform well in use cases linked with EO. However, most end-users interested in processing this kind of imagery might not have the technical know how or time to learn how to use them. We propose to facilitate the proliferation of novel ML frameworks in the area of EO by creating a solution which abstracts as much as possible the underlying technological stack, exposing only what is strictly necessary for the EO expert user.

Some of the generic technical objectives of the ML4EO project are to:

  • Investigate and create a novel technology, based on the latest ML developments, for the integration and processing of large EO and non-EO datasets
  • Facilitate the development/enhancement of the classical data processing workflows by making available big data tools and models as services and exploiting new paradigms in EO;
  • Showcase for testing and evaluating the viability of the ML techniques implemented in ML4EO for operational use;
  • Contribute to a better understanding of some Romanian specific environmental problems by using new approaches in EO processing and by involving new national and regional stakeholders;
  • Create robust opportunities for Romanian entities to remain at the forefront of science, technology and innovation in the exploitation of EO data;
  • Develop a technology that will ready to be used in conjunction with the future Romanian Copernicus Collaborative Ground Segment.