Copernicus Data Space Ecosystem establishes public cloud processing for earth observation data

Earth observation (EO) data are remote sensing image data obtained by air-borne or space-borne sensors, which has the characters of distribution and heterogeneity. Because these data are owned by different space agencies. With Petabyte-scale data volumes of Earth Observation (EO) obtained, the EO data has been one of the most important part of big data. The characters of distribution and heterogeneity of big EO data storage and processing systems owned by different space agencies brings challenges to integrate and use these data. To meet the big EO data challenges, the new generation Spatial data infrastructure is needed to provide the solution and implementation technique to big EO data storage, access, process and analysis. The concept and architecture of Next-generation national EO Spatial Data Infrastructure (EO-SDI) is proposed in the paper. Key techniques are introduced to solve the complexity of distributed and heterogeneous EO data integration and on-demand processing. An interoperable national EO data system of systems is developed to integrate distributed EO archives of eight EO data centers.

Earth observation (EO) data play a crucial role in monitoring ecosystems and environmental processes. Time series of satellite data are essential for long-term studies in this context. Working with large volumes of satellite data, however, can still be a challenge, as the computational environment with respect to storage, processing and data handling can be demanding, which sometimes can be perceived as a barrier when using EO data for scientific purposes. In particular, open-source developments which comprise all components of EO data handling and analysis are still scarce. To overcome this difficulty, we present Tools for Analyzing Time Series of Satellite Imagery (TATSSI), an open-source platform written in Python that provides routines for downloading, generating, gap-filling, smoothing, analyzing and exporting EO time series. Since TATSSI integrates quality assessment and quality control flags when generating time series, data quality analysis is the backbone of any analysis made with the platform. We discuss TATSSI’s 3-layered architecture (data handling, engine and three application programming interfaces (API)); by allowing three APIs (a native graphical user interface, some Jupyter Notebooks and the Python command line) this development is exceptionally user-friendly. Furthermore, to demonstrate the application potential of TATSSI, we evaluated MODIS time series data for three case studies (irrigation area changes, evaluation of moisture dynamics in a wetland ecosystem and vegetation monitoring in a burned area) in different geographical regions of Mexico. Our analyses were based on methods such as the spatio-temporal distribution of maxima over time, statistical trend analysis and change-point decomposition, all of which were implemented in TATSSI. Our results are consistent with other scientific studies and results in these areas and with related in-situ data.

The growing availability of Earth Observation (EO) data enables monitoring of terrestrial ecosystems at unprecedented spatio-temporal resolutions. In practice, however, effective use of EO data remains constrained by substantial technical barriers. Working with raw, multi-modal EO imagery requires specialised domain expertise, large data transfers, access to high-performance computing infrastructure, and advanced machine learning (ML) skills. These requirements limit the accessibility of EO-based analytics for many downstream applications.Geospatial foundation models (GFMs) provide a promising alternative by learning general-purpose representations from large volumes of unlabeled EO data. By decoupling representation learning from downstream task modelling, GFMs allow users to exploit expressive features from modern deep learning models with limited EO or deep learning expertise and modest computational resources.In this work, we investigate embedding-based GFM workflows for forest disturbance monitoring, where timely inference and regional customisation are often more critical than maximising absolute predictive accuracy. Forest disturbances such as logging, windthrows, fires, pests, and diseases can occur abruptly and require rapid detection to support conservation, policy-making, and risk-management efforts.Machine learning methods for forest disturbance detection from EO data are well established and have shown strong performance on regional benchmarks. However, much of this work remains confined to academic demonstrations and is rarely translated into operational monitoring systems. Existing forest monitoring tools, including those aggregated by Global Forest Watch, typically rely on region- and sensor-specific models with limited feature expressivity. These systems may benefit from the rich multi-modal and spatio-temporal representations learned by GFMs, provided such embeddings can be accessed through scalable and practical deployment pipelines.We build on a pipeline designed to deliver on-demand, location- and time-specific geospatial embeddings as a service. Embeddings are generated server-side from raw EO data, compressed, and distributed as lightweight representations. End users interact only with these embeddings, which can be analysed using simple models such as linear probes or small decoders. This approach removes the need for the user to manipulate raw EO data, download large multi-modal datasets, or train and deploy large deep learning models, enabling rapid adaptation to local contexts with limited annotations and modest computational resources.We present preliminary results demonstrating the feasibility of this approach for forest disturbance detection and discuss its strengths and limitations relative to bespoke, fully supervised image-based models. While GFMs may not be optimal for applications with abundant annotations and stringent accuracy requirements, embedding-based services are particularly well suited to time-sensitive and regionally adaptive monitoring scenarios. Overall, this work illustrates how releasing geospatial embeddings as a product or service can lower barriers to EO-based forest monitoring and support faster, more inclusive environmental decision-making.

Collaborative Open Science is essential to addressing complex challenges whose solutions prioritize integrity and require cross-domain integrations. Today, building workflows, processes, and data flows across domains and sectors remains technically difficult and practically resource intensive, creating barriers to whole-systems change. While organizations increasingly aim to demonstrate accountability, they often lack the tools to take action effectively. By making it simple to connect data and platforms together in transparent, reusable and reproducible workflows, the OGC Open Science Persistent Demonstrator (OSPD) aims to enable responsible innovation through collaborative open science. The OSPD focuses specifically on using geospatial and earth observation (EO) data to enable and demonstrate solutions that create capacity for novel research and accelerate the practical implementation of this research.Collaborative Open Science and FAIR (Findable, Accessible, Interoperable, Reusable) data are widely recognized as critical tools for taking advantage of the opportunities created through addressing complex social and environmental challenges. To date, many millions have been invested in hundreds of initiatives to enable access to analytical tools, provide data management, data integration and exchange, translate research results, and support reproduction and testing of workflows for new applications. These investments have resulted in a plethora of new data, protocols, tools and workflows, but these resources frequently remain siloed, difficult to use, and poorly understood, and as a result they are falling short of their full potential for wider impact and their long term value is limited.This presentation will illustrate how the OGC OSPD Initiative, through its design, development and testing activities, provides answers to leading questions such as:How can we design Open Science workflows that enable integration across platforms designed for diverse applications used in different domains to increase their value? How can we lower barriers for end users (decision makers, managers in industry, scientists, community groups) who need to create Open Science workflows, processes, and data flows across domains and sectors remains technically difficult and practically resource intensive, creating? How can Open Science workflows and platforms enable collaboration between stakeholders in different domains and sectors? How can we empower organizations to demonstrate accountability in their analytical workflows, data, and representations of information through Open Science? What Open Science tools do organizations need to take action effectively? How can Open Science and FAIR data standards practically support accountability? How can we make it simple to connect data and platforms together in transparent, reusable and reproducible (FAIR) workflows? What are the specific challenges of using geospatial, earth observation (EO), and complementary data in this context?

ArtEO is a non-profit initiative established with the simple aim of bringing Earth observation (EO) and other environmental data closer to artists and creatives. The mission of ArtEO is to facilitate easier access to data and imagery for artists, musicians, storytellers and creative professionals who wish to engage with climate change and environmental subjects. In the first phase of the initiative (‘Pioneer Phase’), ArtEO has worked with a diverse range of multidisciplinary artists who have produced work in many genres, ranging from large scale sculpture to immersive projections, movement-based performance art to video work, software-based sonification to powerful musical composition. These works have been showcased in a number of major art-science events, and in climate communications and innovation contexts, including the Ars Electronica Festival, MTFLabs, EGU, IAC and elsewhere.ArtEO takes a specific approach to supporting artistic and creative engagement with EO data that is often distinct from other forms of science communication, data visualization and infographics. However, there are some overlaps and they share a common goal of achieving greater reach, visibility and impact with the data. There is an opportunity for cross-learning between all of these areas and this session will provide an insight into the process and methodologies undertaken by artists working with EO in the ArtEO Pioneer Phase, and what this has revealed about the challenges for the wider public of engaging with EO data and imagery. and about ArtEO’s aim is to achieve significant public reach with both the artworks themselves and the data utilised.

At present, accessing and processing Earth Observation (EO) data on different cloud platforms requires users to exercise distinct communication strategies as each backend platform is designed differently. The openEO API (Application Programming Interface) standardises EO-related contracts between local clients (R, Python, and JavaScript) and cloud service providers regarding data access and processing, simplifying their direct comparability. Independent of the providers’ data storage system, the API mimics the functionalities of a virtual EO raster data cube. This article introduces the communication strategy and aspects of the data cube model applied by the openEO API. Two test cases show the potential and current limitations of processing similar workflows on different cloud platforms and a comparison of the result of a locally running workflow and its openEO-dependent cloud equivalent. The outcomes demonstrate the flexibility of the openEO API in enabling complex scientific analysis of EO data collections on cloud platforms in a homogenised way.

Models of daily runoff from seasonal snowpacks and glaciers require knowledge or assumptions about the decline in snow covered area (SCA). Some semi-distributed models rely on satellite data as an input in addition to meteorological data but general purpose hydrological models with a snow component do not normally use earth observation (EO) data. EO data have the potential to verify or update SCA predictions generated by these models, but comparison is hampered by the unrealistic assumption in most models of spatially uniform snow water-equivalent (SWE) within entire zones, SO that SCA decline is stepped. Two possible solutions are either to allow a stepped SWE distribution within a sub-area, or to assume uniform melt over a non-uniform snowpack within a sub-area. In both approaches melt is converted into a reduction in SCA as well as SWE allowing snowpack depletion to be compared directly with EO data. Two examples are given in which EO data is used to verify (and in one case update) SCA. The HBV model is applied to a basin in Arctic Sweden and a recently developed glacier runoff model is applied to a basin in the Swiss Alps. Landsat TM data of both basins revealed considerably less snow than simulated by the models. TM data for the Swedish basin show that only glacier zones were 100% snow covered. Despite over-predicting SCA both models achieved very good discharge fits. It is argued that runoff models should correctly simulate the hydrological system state variables if they are to be transferred to different environments or new climate scenarios with confidence, and that EO data can play a valuable role in this.

The role of Earth observation (EO) data in addressing societal problems from environmental through to humanitarian should not be understated. Recent innovation in EO means provision of analysis ready data and data cubes, which allows for rapid use of EO data. This in combination with processing technologies, such as Google Earth Engine and open source algorithms/software for EO data integration and analyses, has afforded an explosion of information to answer research questions and/or inform policy making. However, there is still a need for both training and validation data within EO projects – often this can be challenging to obtain. It has been suggested that citizen science can help here to provide these data, yet there is some perceived hesitancy in using citizen science within EO projects. This paper reports on the Citizen Science 4 EO (Citizens4EO) project that aimed to obtain an in-depth understanding of researchers’ and practitioners’ experiences with citizen science data in EO within the UK. Through a mixed methods approach (online and in-depth surveys and a spotlight case study) it was found that although the benefits of using citizen science data in EO projects were many (and highlighted in the spotlighted “Slavery from Space” case study), there were a number of common concerns around using citizen science. These were around the mechanics of deploying citizen science and the unreliability of a potentially misinformed or undertrained citizen base. As such, comparing the results of this study with those of a similar survey undertaken in 2016, it is apparent that progress towards optimizing citizen science use in EO has been incremental but positive with evidence of the realization of the benefits of citizen science for EO (Citizens4EO). As such, we conclude by offering priority action areas to support further use of citizen science by the EO community within the UK, which ultimately should be adopted further afield.

Abstract. Being able to accurately estimate parameters characterising land surface interactions is currently a key scientific priority due to their central role in the Earth's global energy and water cycle. To this end, some approaches have been based on utilising the synergies between land surface models and Earth observation (EO) data to retrieve relevant parameters. One such model is SimSphere, the use of which is currently expanding, either as a stand-alone application or synergistically with EO data. The present study aimed at exploring the effect of changing the atmospheric sounding profile on the sensitivity of key variables predicted by this model assuming different probability distribution functions (PDFs) for its inputs/outputs. To satisfy this objective and to ensure consistency and comparability to analogous studies conducted previously on the model, a sophisticated, cutting-edge sensitivity analysis (SA) method adopting Bayesian theory was implemented on SimSphere. Our results did not show dramatic changes in the nature or ranking of influential model inputs in comparison to previous studies. Model outputs examined using SA were sensitive to a small number of the inputs; a significant amount of first-order interactions between the inputs was also found, suggesting strong model coherence. Results showed that the assumption of different PDFs for the model inputs/outputs did not have an important bearing on mapping the most responsive model inputs and interactions, but only the absolute SA measures. This study extends our understanding of SimSphere's structure and further establishes its coherence and correspondence to that of a natural system's behaviour. Consequently, the present work represents a significant step forward in the global efforts on SimSphere verification, especially those focusing on the development of global operational products from the model synergy with EO data.

Abstract. The exponential growth of Earth Observation (EO) data generated by satellites demands scalable, efficient, and interoperable methods not only for storing and managing, but also for distributing data packages tailored to diverse use cases. The Open Container Initiative (OCI) registries, together with the OCI artifact specifications, present a promising framework for packaging, exchanging, and managing EO-derived and combined datasets. Originally developed for software containers, OCI registries offer key capabilities such as content-addressable storage, data integrity verification, cryptographic attestation, layered packaging, and version control. A notable advantage is their ability to act as access gateways—enforcing access control at the artifact level without requiring direct exposure of the underlying storage backends (e.g., S3, GCS, Azure Blob, NFS, Ceph, IPFS). The ubiquity of OCI registries—spanning public platforms, managed enterprise services, and open-source deployments—makes them a practical foundation for distributing EO data across heterogeneous environments without custom infrastructure. This paper investigates the applicability of today’s OCI registry ecosystem to EO data pipelines, evaluating both strengths and current limitations in handling large, complex, and dynamic datasets. We explore design conventions and layout strategies to align EO products with the OCI artifact model, with a focus on metadata representation, access efficiency, and storage reuse. By comparing self-contained data packages with modular, layered asset stores, we highlight trade-offs in retrieval performance, interoperability, and client complexity. Recent trends in machine learning model distribution further underscore the growing relevance of OCI-based artifacts for scientific and geospatial workflows. Ultimately, this research positions OCI artifacts as a viable foundation for scalable, standards-aligned, and interoperable EO data handling—paving the way toward more streamlined and resilient data supply chains in the EO domain.

Abstract. Landslide mapping benefits from the ever increasing availability of Earth Observation (EO) data resulting from programmes like the Copernicus Sentinel missions and improved infrastructure for data access. However, there arises the need for improved automated landslide information extraction processes from EO data while the dominant method is still manual delineation. Object-based image analysis (OBIA) provides the means for the fast and efficient extraction of landslide information. To prove its quality, automated results are often compared to manually delineated landslide maps. Although there is awareness of the uncertainties inherent in manual delineations, there is a lack of understanding how they affect the levels of agreement in a direct comparison of OBIA-derived landslide maps and manually derived landslide maps. In order to provide an improved reference, we present a fuzzy approach for the manual delineation of landslides on optical satellite images, thereby making the inherent uncertainties of the delineation explicit. The fuzzy manual delineation and the OBIA classification are compared by accuracy metrics accepted in the remote sensing community. We have tested this approach for high resolution (HR) satellite images of three large landslides in Austria and Italy. We were able to show that the deviation of the OBIA result from the manual delineation can mainly be attributed to the uncertainty inherent in the manual delineation process, a relevant issue for the design of validation processes for OBIA-derived landslide maps.

Urbanization as a global phenomenon is a multifaceted process, affecting sustainable development in and around urban areas.Exploring the multi-characteristics of urbanization and their environmental effects is crucial for achieving Sustainable Development Goals (SDGs) in global cities.Most existing research monitored urbanization and urban sustainability from a single perspective with limited temporal and/or spatial coverage.Therefore, the primary objective of this thesis is to enhance a comprehensive understanding of global urbanization and sustainability as well as their nexus at the grid/neighborhood level, by integrating multisource Earth Observation (EO) data and social data.Chapter 2 provides a comprehensive characterization of global multifaceted urbanization in terms of population, built-up structure, and greenness per 5 × 5 km 2 inhabited grid from 1975 to 2015.The Global Human Settlement Layer and MODIS/GIMMS NDVI were used to detect long-term dynamics of built-up patches and greenness.Results emphasize the multifaceted nature of urbanization that varies greatly across regions and times.The urbanization types, identified by a self-organizing map algorithm, reveal diverse patterns across continents and urban-suburban-rural settlements.Urbanization processes became less intense, more compact, and "greener" over the recent period (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015) compared to previous periods .This chapter offers a comprehensive application of multi-source EO data to the understanding of global multifaceted urbanization with many spatial and temporal variations.Chapter 3 presents global monitoring of the land-energy-air nexus in terms of land use efficiency (SDG 11.3.1),energy efficiency (SDG 7.3.1),and air quality (SDG 11.6.2) at 1 km resolution from 2000 to 2015.Multi-source EO and statistical data were integrated to assess the grid-level SDGs.For instance, statistical energy consumption was downscaled by statistical models based on gridded night-time lights and population data.Our maps highlight distinct urban-rural gradients and the Global North-South differences.Urban areas are more sustainable than rural areas in the Global South but are less sustainable than suburbs in the Global North.Trade-offs in the land-energy-air nexus are mostly related to energy efficiency versus air quality in urban areas.This chapter demonstrates how EO data can support contextspecific and win-win-win strategies for achieving land-energy-air SDGs from a global perspective but in high spatial detail.Chapter 4 develops a multi-level approach and supply-demand concept to explore urban green inequality and its mismatches with human demand across neighborhoods in New York, Amsterdam, and Beijing in 2020.The ESA World Cover, Open Street Map, and neighborhoodlevel census were combined to assess green supplies.The land surface temperature, PM2.5, income, and age structure were used to quantify human demands.Results show that the urban green inequality based on Gini coefficients and supply-demand relations vary by metrics and cities.Low supply-high demand neighborhoods are mainly scattered around the city center in study cities.This chapter stresses the importance of assessing demand-based inequities in urban green research, and can guide neighborhood-specific strategies for urban green allocation towards demand-oriented equity in study cities.Chapter 5 explores the effects of urban-rural interactions on regional developments of population, employment, and GDP per capita at the NUTS-3 level in Europe between 2003 and 2018.We used geographical proximity to measure potential interactions between urban, intermediate, and rural regions.Results show that urban-rural interactions are generally related to benefits for both urban and rural regions, although the effects differ between periods and between different dimensions of regional development.Initial regional performance is significantly associated with regional development.This chapter emphasizes the importance of considering urban-rural interactions in understanding the overall health of regional systems and the potential for urban-rural synergies.Chapter 6 unravels global nexuses between urbanization and environmental livability in terms of greenness, summertime temperature, and air quality per 5 × 5 km 2 inhabited grid between 2000 and 2020.Multi-source EO data were used to quantify multifaced 4D urbanization and green-climate-air livability trends.The Geographically Weighted Regression models were adopted to analyze the urbanization-green-climate-air nexus by accounting for both the global, general effect, and local, various effects.Results present large inter-and intra-city variations in urbanization effects on green-climate-air trends with opposite directions across continents.This chapter can provide global and local insights into which urbanization process should be intervened to promote urbanization into a sustainable solution rather than a threat.This PhD thesis contributes to the data revolution of integrating Earth Observation and social data for urban science, enabling a comprehensive understanding of global urbanization and critical sustainability trends with local variations and characteristics.By providing actionable information for context-specific strategies instead of a 'one-size-fits-all' policy, this thesis builds a science-policy bridge towards a sustainable and livable urban future.

RF-CLASS: A remote-sensing-based flood crop loss assessment cyber-service system for supporting crop statistics and insurance decision-making

Wildfire Monitoring Using Satellite Images, Ontologies and Linked Geospatial Data

Earth Observation (EO) data plays a crucial role in research and applications related to environmental monitoring, enabling informed decision-making. However, the continuously increasing volume and diversity of EO data, distributed across multiple platforms and varying formats, pose challenges for easy access and the development of scalable and reproducible workflows.openEO addresses these challenges by providing a community-driven, open standard for unified access to EO data and cloud-native processing capabilities. It supports researchers to develop interoperable, scalable and reproducible workflows that can be executed using various programming languages (Python, R or JavaScript).openEO has become a cornerstone technology across major initiatives in agriculture, natural capital accounting, and land-cover monitoring. In ESA’s WorldCereal project, it provides the scalable framework needed to process global Sentinel-1 and Sentinel-2 time series and integrate advanced machine-learning models, enabling dynamic 10-meter cropland and crop-type maps. It also supports the Copernicus Global Land Cover service and its tropical forestry component by delivering consistent and repeatable processing chains for annual 10-meter land-cover products, which are crucial for policy reporting and SDG monitoring. Beyond land cover, openEO supports efforts like ESA's World Ecosystem Extent Dynamics project by creating reproducible ecosystem-extent mapping and change detection maps — key elements for biodiversity and environmental management.Building on this foundation, the openEO Federation, now integrated within the Copernicus Data Space Ecosystem (CDSE), provides seamless access to distributed Earth observation data and processing resources through a single, unified interface. By connecting multiple backends, it removes the need to juggle separate accounts or APIs and enables cross-platform workflows over datasets hosted by platforms such as Terrascope and CDSE.openEO also strongly supports FAIR (Findable, Accessible, Interoperable, Reusable) principles. It exposes rich metadata, relies on standardised processes, and encourages the use of reusable workflow definitions. This promotes transparency, reproducibility, and the sharing of algorithms and data across research and operational communities. The approach has been validated in several large-scale implementations, including ESA’s WorldCereal and the JRC’s Copernicus Global Land Cover and Tropical Forestry Mapping and Monitoring Service (LCFM), demonstrating its maturity for both research and production environments.By enabling reusable, federated, and reproducible Earth observation workflows, openEO is helping to build a more interoperable and efficient computational ecosystem, one that supports scalable innovation, collaboration, and long-term operational monitoring. Therefore, in this session, we aim to spark discussion on how openEO enables federated, FAIR-compliant, and reproducible workflow approaches for large-scale Earth observation applications.