The Global Earth Monitor project addresses the challenge of continuously monitoring large areas in a sustainable and cost-effective manner. The project aims to create a new, breakthrough model for the use of Earth observation data that will dramatically improve the use of Copernicus data and enable, for the first time, continuous monitoring of the planet on a global/regional scale at a sustainable cost. Breakthrough innovations in technology and methodology are planned, where a proprietary concept of Adjustable Data Cubes (a combination of static and dynamic data cubes) will be developed and integrated with the EO -oriented open-source machine learning (ML) framework eo-learn.

The main impacts of Global Earth monitor are those that will influence the whole of human society by providing tools for better environmental monitoring and management - leading to a common goal: a sustainable environment

The key objectives are:
To take full advantage of the amount of data available, Copernicus needs a new model of data use - new processes that can run continuously - monthly, weekly or even daily - replacing brute force methods with smart techniques that scale cost-effectively. New methods of Earth observation (EO) are needed that work in a sustainable way - adding more value than their cost - at least on a continental if not global scale, able to automatically improve accuracy and detect changes as they occur. Users of data from Earth observations need a way to proactively monitor the entire globe and automatically look for changes and events worthy of closer examination. The primary goal of the Global Earth Monitor (GEM) project is to enable commercially viable continuous monitoring of the Earth, driven by a transition from traditional "strip mode" monitoring to "spot mode" monitoring (discovery of relevant details). This approach is based on the global monitoring and data use model of Global Earth Monitor.

The first reporting period has seen a wide scope of activities. Initial focus has been on system, data, algorithm, and user analyses, all oriented towards finding most needed data and functionalities to address the challenges of cost-effective global Earth monitoring. Global earth monitor tasks revolved mostly around setting up the basis for the second part of the project. The main tasks were to create an inventory and consolidate the existing Earth observations, weather and other data, identify uses, needs, and wishes from consortium partners, stakeholders and outside users and apply these to the requirements and functionalities of the services and Global Earth monitor in general. We have then moved in parallel; while making sure the services conform to these specifications, implementing extra, refactoring existing to support missing functionalities, we have also made sure that all these were accessible through the Global Erath monitor framework: eo-learn. Early in the project we have decided to take into (strong) consideration also the Global Earth monitor use-cases. Although the use-cases are just starting, we have already discussed and defined the AOIs, (area of interest) prepared (retrieved, cleaned) majority of ground truth (GT) data, and for some set up the baseline models.
We aim at providing a set of tools to make prototyping of complex EO (erath observations) workflows as easy, fast, and accessible as possible. In the first reporting period, the functionality of eo-learn has been expanded to include weather data via the meteoblue services, and new data collections and features of Sentinel Hub services. Version 1.0 which is expected to be released with the upcoming deliverable in the next months, will bring in improvements oriented towards scalability and big data capabilities, in line with system requirements and design.

In the current Earth observations business, there seem to be two distinct options emerging: either build a series of data products and sell them to clients or create a platform for the clients to build bespoke products for themselves. One of the most important values of the Sentinel Hub (and consequently GEM) is the consolidated way to retrieve the data. Regardless, if the data is from Copernicus missions, VHR commercial datasets, or if it is user’s own data ingested to Sentinel Hub, the API to request said data is the same. This way, users can really focus on what to do with the data, not on how to get it. Combine this streamlined data access with eo-learn (bringing in gateways to weather. climate and other non EO data), and the consolidation based on EOTasks and EOWorkflows, users can focus on trying out new things, researching and implementing ideas to produce value adding results from the plethora of data available to them.
So again, within Global Erath monitor we are trying to balance both opportunities: using services to provide data and develop tools to make prototyping of complex EO workflows as easy, fast, and accessible as possible.Within GEM use-cases we will make use of all these capabilities for two purposes. Firstly, to showcase the capabilities of the increased performance, cost-effectiveness and scalability of services and framework for continuous monitoring using drill-down mechanism. And secondly, to demonstrate the adoption of use-case results for the decision making within industrial (e.g. map making), societal (e.g. conflict pre-warning maps) and other domains (e.g. crop identification for common agricultural policy). GEM’s services will be developed as a next evolutionary step of Sentinel Hub. As Sentinel Hub is already a part of 4 DIAS-es, GEM will thus enrich the services DIAS-es offer to their user community. The aDC capabilities will be directly integrated and exposed as one of the DIAS services and be useful for various large-scale processing tasks, for commercial users and governmental institutions.
The Global Earth monitor framework showcased with Jupyter notebooks will hopefully spark new ideas on how to effectively use EO, weather and other data sources to monitor, assess, and understand two of the biggest environmental challenges that humanity faces nowadays: climate change and loss of biodiversity, both part of EU's Global Strategy and UN’s 2030 Agenda for Sustainable Development.
We believe that the best measure for the impact is how much the tools, resulting from the project, are being used. As already said in the introduction, due to fact that eo-learn is open-source software, we do not have exact information on all users. However, the information we have, are remarkable:
• Active engagement of the users on GitHub (800+ stars, 59 subscriptions for notifications, 250+ forks, 30 contributors, used by 40+ other GitHub projects).
• More than 50.000 reads of the eo-learn blog posts series with 600+ fans.
• 30000+ installations of eo-learn (via conda and pip)
Global earth monitor project is promoted also on two social media channels: Twitter and LinkedIn. Both channels are continuously gaining followers in the last year.