Copernicus Data Space Ecosystem (CDSE) is the largest and most up-to-date archive of Copernicus data, which are made available in numerous ways, supporting browsing, interactive exploration, downloading and in-depth processing. It serves as the core source of Copernicus Sentinel data. Most importantly, it is a platform designed with integration and federation in mind - composed of a set of open APIs built on well-known standards in Earth Observation (EO), such as STAC, OGC and openEO, as well as typical IaaS ones – Single-Sign-On (SSO) and S3 data storage access. We will present how one can use CDSE to design and prototype applications and then operate it in the Ecosystem in a federated manner, keeping full control over the level of integration - from data access and visualisation and up to user identity, processing integration, and data privacy. We will also discuss federation governance, as well as the combination of publicly funded services and commercial offering, touching on a few typical use cases.

We present the latest development in the Digital Earth Africa (DE Africa) program on application development using analysis-ready data, development of tools for accessing commercial imagery, and evaluation of additional analysis-ready data sources. We discuss the significant achievements and challenges encountered and highlight future work essential for the program’s goal to empower African countries in climate actions.

With Earth Observation (EO) missions generating tens of TB of data every day, much of it available in a free and open manner, systematically observing our planet, the opportunities for society to benefit from this data are endless. There are, however, remaining challenges of reusability, reproducibility, and scalability of the scientific analysis run on EO data. This often results in interesting findings addressing the situation from a year or two ago, due to time required for validation of the data.

Every scientific experiment starts with the data, which needs to be fine-tuned for the specific use-case. We call this "analysis ready data (ARD)". In some cases, for the sake of reusability and comparability, the specifications on how ARD should be prepared, are well defined - CEOS is working hard in this direction. In many other cases, however, the procedures are not yet mature enough to support standardisation. In Earth Observation (EO) field this is especially true, as the whole community is moving from (semi) manually analysing individual scenes, from the time there were any data barely available, to processing of time-series, now that Landsat and Sentinel made this possible. We are now even facing a problem where there is simply too much of data, with PBs of open and commercial imagery being readily available. Machine learning (ML) approach can address the challenge of shifting through data, but ML as well requires data to be pre-processed for purpose. Therefore, it is essential to have facility, which can generate ARD data customised for the specific analysis' requirements. Sentinel Hub is one of such tools.

As Earth Observation (EO) is undoubtedly one of the industries that will benefit the most from the 4th industrial revolution driven by Artificial Intelligence (AI), the European Space Agency (ESA) Centre for Earth Observation (also known as the European Space Research Institute or ESRIN)) has contracted a team of contractors led by SpaceTec Partners to accompany its staff in exploiting opportunities of rapprochement between the AI and EO communities to the largest possible extent. To implement this ambition, the Artificial Intelligence for Earth Observation (AI4EO) initiative builds a community that fosters the interaction between AI and EO experts, developed a custom AI4EO platform, and host three challenges on the platform to tackle grand societal issues. The objectives of the challenges such as air quality or food security are to combine EO with an innovative AI method enabling the development of novel solutions. This paper introduces the AI4EO project and the first challenge.