Success Stories#
Besides serving individual researchers, DeepESDL also operates as a comprehensive service platform for Earth Science projects. Teams benefit from its ready-to-use infrastructure, covering computational resources for scalable data processing, collaborative and tailorable development environments, machine-learning tooling, and data visualization and dissemination services, to meet a wide range of technical needs. Its modular offer, which is available in the Network of Resources, enables each project to pick and configure only the components they require, from turnkey end-to-end solutions to individual services for specific tasks.
DeepESDL has been among the first platforms that integrated with ESA's emerging EarthCODE initiative for making key outputs of research projects reusable and reproducible beyond the lifetime of the activities. To this end, the DeepESDL team has developed tools and integrations to publish workflows encoded in Jupyter notebooks and the resulting datasets in the EarthCODE catalogue and potentially also in ESA's Project Results Repository (PRR). By implementing their research on DeepESDL, users can ensure that they meet ESA's requirements for a FAIR publication of results. The following projects have been or are still successfully doing research enabled by DeepESDL.
Projects#
Baltic AIMS#
The BalticAIMS project set out to strengthen spatial planning across land and sea in the Baltic region by helping planners coordinate actions that minimize human impacts and promote environmental health. It aimed to bring together diverse environmental observations—such as satellite imagery, on-site measurements and model outputs—into a unified perspective on coastal and terrestrial processes EO Science for Society. By delivering this integrated view, the project sought to deepen understanding of how land-based activities affect coastal waters and vice versa, supporting more informed decisions. Ultimately, BalticAIMS focused on enhancing the capacity of regional authorities and stakeholders to plan sustainably for the interconnected land-sea system.
Baltic AIMS uses DeepESDL as a one-stop provider for all required cloud services, including compute, storage, data dissemination, and Viewer as a Service.
Further information: Baltic AIMS website, Baltic AIMS Viewer
DeepExtremes#
The DeepExtremes project aimed at an improved understanding of simultaneous heat and drought events around the world by combining long-term climate records with satellite observations. To this end, key episodes have been examined in detail by comparing affected regions with similar unaffected areas to uncover the patterns driving these compound extremes. The project also sought to develop advanced methods to anticipate such events and share insights with the wider research community.
A key result have been numerous so-called minicubes distributed all over the world containing relevant variables from various soures. These minicubes have been generated within DeepESDL and will now be ingested into ESA's emerging EarthCODE catalogue and then persisted in the ESA's novel Project Results Repository (PRR).
Further information: EO4Society Website, RSC4Earth Website
OBSGESSION#
The OBSGESSION project, funded under the EU Horizon programme, aims to advance the understanding of biodiversity change by combining Earth Observation methods, in-situ measurements, and ecological modelling. A central focus lies on identifying both direct and indirect drivers of biodiversity dynamics and improving the sharing of knowledge to support ecosystem and biodiversity management.
As part of the project, DeepESDL was employed to enable scalable data access and processing in support of the generation of Essential Biodiversity Variables (EBVs) for selected pilot sites. These EBVs provide metrics for monitoring biodiversity change.
Further information: OBSGESSION website
ESA Science Hub courses#
ESA's Science Hub is a facility at ESRIN dedicated to advanced earth system and climate science studies, serving as a centre of excellence for scientific collaboration, networking, and exchange of ideas to address the major science challenges of this decade. It promotes a community-driven response by boosting the scientific output of ESA and its member states through access to ESA expertise, infrastructure, and resources that support innovative research efforts. The Agency is regularly organizing Science Hub challenges, on site courses for students from universities and research institutions of ESA member states.
DeepESDL has been supporting these events by providing a ready-to-use, collaborative development environment, computational resources, and on-site expertise for the efficient implementation of scientific workflows into scalable software.
Further information: February 2024, May 2024, March 2025, July 2025
DeepFeatures#
The land surface influences climate by regulating exchanges of water, energy, and carbon with the atmosphere, making it essential to track changes under climate change. Satellites offer long-term, global observations that reveal shifts in vegetation, soil moisture, land cover, and temperature, which can be analyzed using both statistical methods and modern AI techniques. In this project, AI is applied to combine hundreds of spectral indices (SIs) into a smaller set of features that capture ecosystem dynamics across space and time. These features are organized in a Feature Data Cube, enabling more efficient analysis and supporting studies of vegetation, water, and climate adaptation.
A key outcome has been the creation of multiple medium-sized data cubes across Europe, containing Sentinel-2 L2A data and relevant variables from different sources for validation and analysis. In addition, smaller training cubes have been generated to train the AI models. These data products, generated within DeepESDL, will be integrated into ESA’s emerging EarthCODE catalogue.
Further information: DeepFeatures Website
EO4HEALTH#
The EO4Health Resilience project assesses how EO products can support public health decision-making, scenario analysis, and impact and risk assessments by addressing scientific gaps and stakeholder needs to create a sustainable, long-term initiative integrating EO into health resilience strategies. Leveraging ESA’s prior work, it develops services using EO data to detect spatio-temporal patterns for predicting Vector-Borne Diseases (VBD) and Water-Borne Diseases (WBD). The VBD service expands an Italian West Nile Virus model to cover North Africa and Europe, with outputs validated against ground data, while WBD services refine models for cholera, E. coli, and Vibrio risks in areas like Vembanad Lake and the Baltic Sea. A core component of the activity is the “Resilience & Earth Observation Virtual Observatory” platform, offering user-friendly access to EO and health data, enabling non-specialists to conveniently utilize the outcomes of the project for their work.
The EO4Health Resilience project is using DeepESDL services to collaboratively develop workflows and to process and integrate data sets. Moreover, the Virtual Laboratory has been implemented leveraging xcube viewer as a service, specifically benefitting from its potential for extensions, customisation, and tailoring.
Further information: EO4Health Website, EO4Health Virtual Observatory
EO-LINCS#
EO-LINCS aims to lower the technological barriers for integrating Earth Observation (EO) data into carbon cycle research by treating data production and scientific analysis as a unified system, co-designing bespoke data pipelines with scientific and technical partners across four case-study sites. Its integrated approach spans data infrastructure, software engineering, ground measurements, and carbon cycle modeling to evaluate how multiple EO datasets enhance understanding of net ecosystem exchange, interannual variability, drought responses, and disturbance effects. By iteratively combining EO streams with explainable machine‐learning within frameworks like FLUXCOM-X, EO-LINCS seeks to clarify key carbon fluxes and states, ensuring EO products directly inform and improve carbon cycle science.
EO-LINCS uses DeepESDL as collaborative development environment for the science partners and for data access and processing. For the project, the xcube library has been extended by a multistore, which enables users to easily create data cubes from multiple sources.
Further information: EO-LINCS website, xcube Multistore
WQ Forecasting#
This project developed software that includes the forecast processor authored for the Forecasting Water Quality from Space study. The objective of the study is to provide time series forecasts of chlorophyll concentration. Predicted chlorophyll concentration is derived from EO-based retrievals of chlorophyll concentration and several covariates, which are routinely provided by reanalysis and forecast services based on biological, chemical, physical, and meteorological models.
Further information: WQ Forecasting website