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Over the last decades, to better proceed towards global and local policy goals, there was an increasing demand for the scientific community to support decision-makers with the best available knowledge. Scientific modeling is key to enable the transition from data to knowledge, often requiring to process big datasets through complex physical or empi...
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The COVID-19 pandemic poses major challenges for all sectors of society, including scientists faced with abrupt disruptions and redirections of research and higher education. The consequences of this crisis will disproportionately impact early-career scientists; especially those from communities historically under-represented, disadvantaged and/or...
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... (a) to re-establish a strong connection with the GEOSS Community working closely with each data provider as undertaken by some recent efforts (Roncella Zhang, et al. 2022;, rescheduling regular GEOSS data Providers and Users workshops, also to the aim of formally identifying gaps and requirements to be subsequently implemented by the GEOSS platform and by the data providers (i.e. to tackle the low metadata quality issue); where useful, to jointly define community tailored views and portals; (b) to differentiate the needs of platform clients versus users, responding with increasingly specialized services; (c) to present, in a clear manner, an overall design where the GKH is part of and advances the GEOSS Platform; a possible synergy could be achieved by enabling an interoperable data workflow that, starting from observations published to the GEOSS platform, produces the products available on the GKH (e.g. exploiting knowledge generation frameworks such as the VLab [Santoro, Mazzetti, and Nativi 2020;Santoro, Mazzetti, and Nativi 2023]). ...
The GEOSS Platform is a key contribution to the goal of building the Global Earth Observation System of Systems (GEOSS). It enables a harmonized discovery and access of Earth observation data, shared online by heterogeneous organizations worldwide. This work analyzes both what is made available in the GEOSS Platform by the data providers and how users are utilizing it including multiyear trends, updating a previous analysis published in 2017. The present statistics derive from a 2021 EOValue report funded by the European Commission. The offer of GEOSS Platform data has been the object of various analyses, including data provider characterization, data sharing trends, and data characterization (comprising metadata quality analysis, thematic analysis, responsible party identification, spatial–temporal coverage). GEOSS data demand has also been the object of several analyses, including data consumer characterization, utilization trends, and requested data characterization (comprising thematic analysis, spatial–temporal coverage, and popularity). Among the findings, a large amount of shared data, mostly from satellite sources, emerges with an issue of low metadata quality and related discovery match. Moreover, the trend in usage is decreasing. Therefore, the progressive disconnection of the GEOSS platform from its data Providers and Users and other possible causes are also reported.
... There are two types of service chains that are frequently used in real applications: one type contacts a single service using a fixed binding, and the other type connects a service using a defined arrangement based on a service chain such as BPEL (Business Process Execution Language). Despite the fact that numerous automatic or semi-automatic service composition techniques have been put forth by researchers, the majority of them necessitate the creation of executable service composition templates first [30,31]. After that, the service parameters, interfaces, and metadata are described at the syntax level, services are found through keyword matching, and executable service chains are generated [32]. ...
Land cover change (LCC) is increasingly affecting global climate change, energy cycle, carbon cycle, and water cycle, with far-reaching consequences to human well-being. Web service-based online change detection applications have bloomed over the past decade for monitoring land cover change. Currently, massive processing services and data services have been published and used over the internet. However, few studies consider both service integration and resource sharing in land cover domain, making end-users rarely able to acquire the LCC information timely. The behavior interaction between services is also growing more complex due to the increasing use of web service composition technology, making it challenging for static web services to provide collaboration and matching between diverse web services. To address the above challenges, a Dynamic Service Computing Model (DSCM) was proposed for monitoring LCC. Three dynamic computation strategies were proposed according to different users’ requirements of change detection. WMS-LCC was first developed by extending the existing WMS for ready-use LCC data access. Spatial relation-based LCC data integration was then proposed for extracting LCC information based on multi-temporal land cover data. Processing service encapsulation and service composition methods were also developed for chaining various land cover services to a complex service chain. Finally, a prototype system was implemented to evaluate the validity and feasibility of the proposed DSCM. Two walk-through examples were performed with GlobeLand30 datasets and muti-temporal Landsat imagery, respectively. The experimental results indicate that the proposed DSCM approach was more effective and applicable to a wider range of issues in land cover change detection.
... The GEOSS Platform (formerly called GCI: GEOSS Common Infrastructure) was created as the technological tool to implement interoperability among the ecosystem of enterprise systems and be the cornerstone around which GEOSS is implemented (Boldrini, Hradec, Craglia, & Nativi, 2021;Craglia, Hradec, Nativi, & Santoro, 2017;Nativi et al., 2015). Recently, the GEOSS concept is evolving towards the Digital Twin pattern enabled by a flexible and scalable digital ecosystem (Guo et al., 2020;Nativi, Mazzetti, & Craglia, 2021;Santoro, Mazzetti, & Nativi, 2020). Furthermore, the new GEOSS platform will enable model sharing such as demonstrated in the GEO Plenary held in Canberra in 2019 (Ollier, 2019). ...
This paper is the second of a series that describes some of the main dataset resources presently shared through the GEOSS Platform. The GEOSS Platform was created as the technological tool to implement interoperability among the Global Earth Observation System of Systems (GEOSS); it is a brokering infrastructure that presently brokers more than 190 autonomous data catalogs and information systems. This paper is focused on the analysis of the NextGEOSS datasets describing the data publishing process from NextGEOSS to the GEOSS platform. In particular, both the administrative registration and the technical registration were taken into consideration. One of the most important data shared by the GEOSS Platform are the NextGEOSS datasets: the present study provides some insights in terms of GEOSS user searches for NextGEOSS data.
... To facilitate the reproducibility of the results obtained in the SMIG2 pilot city (i.e., Bari, southern Italy), the dasymetric mapping model [29,30] was published using the Virtual Earth Laboratory (VLab) framework [91] in two versions (a basic one, and an advanced one for expert users). ...
The purpose of this research is to demonstrate the strong potential of Earth-observation (EO) data and techniques in support of migration policies, and to propose actions to fill the existing structural gaps. The work was carried out within the “Smart URBan Solutions for air quality, disasters and city growth” (SMURBS, ERA-PLANET/H2020) project. The novelties introduced by the implemented solutions are based on the exploitation and synergy of data from different EO platforms (satellite, aerial, and in situ). The migration theme is approached from different perspectives. Among these, this study focuses on the design process of an EO-based solution for tailoring and monitoring the SDG 11 indicators in support of those stakeholders involved in migration issues, evaluating the consistency of the obtained results by their compliance with the pursued objective and the current policy framework. Considering the city of Bari (southern Italy) as a case study, significant conclusions were derived with respect to good practices and obstacles during the implementation and application phases. These were considered to deliver an EO-based proposal to address migrants’ inclusion in urban areas, and to unfold the steps needed for replicating the solution in other cities within and outside Europe in a standardized manner.
... In the past, the GEOSS platform has evolved a lot to address new challenges and leverage the technological developments. A new advanced GEOSS platform is under development to embrace the opportunities offered by the digital transformation of society (Guo et al., 2020;Nativi, Mazzetti, & Craglia, 2021;Santoro, Mazzetti, & Nativi, 2020). Notably, the new GEOSS platform will enable model sharing such as demonstrated in the last GEO Plenary (Ollier, 2019). ...
This paper is the first of a series that describes some of the main dataset resources presently shared through the GEOSS Platform. The GEOSS Platform has been created to provide the technological tool to implement the Global Earth Observation System of Systems (GEOSS); it is a brokering infrastructure that presently brokers more than 190 autonomous data catalogs and information systems. The paper analyses the China Satellite datasets and describes the data publishing process from China GEOSS Data Provider to the GEOSS Platform considering both administrative registration as well as the technical registration. The China Satellite datasets are considered as one of the most important satellite data shared by the GEOSS Platform. The analysis provides some insights as well about GEOSS user searches for China Satellite datasets.
... We used the VLab platform (Mazzetti et al., 2018;Santoro et al., 2020) to arrange and provide a workflow (Fig. 6a) with data processing and analysis. VLab stands for Virtual Laboratory Platform and facilitates the publication of scientific workflows to support evidence-based decision-making. ...
... Therefore, iCUPE developed a set of pilot workflows and actions including data access that target the definition, support and monitoring capability for EVs and SDGs. In iCUPE we used the Virtual Earth Laboratory (VLab) (Santoro et al., 2020) which is a framework addressing some of these challenges to facilitate the generation of knowledge based on the use of scientific models; it automates the technical tasks required to execute a model on different computing infrastructures, minimizing as much as possible interoperability requirements for both model developers and users. The use of the VLab platform has been tested on integrating multi-source data (remote and terrestrial sensing) into a complex workflow aimed at assessing the snow seasonality reducing the knowledge gap between the skilled developer and the potential end-user. ...
Integrative and Comprehensive Understanding on Polar Environments (iCUPE) project developed 24 novel datasets utilizing in-situ observational capacities within the Arctic or remote sensing observations from ground or from space. The datasets covered atmospheric, cryospheric, marine, and terrestrial domains. This paper connects the iCUPE datasets to United Nations’ Sustainable Development Goals and showcases the use of selected datasets as knowledge provision services for policy- and decision-making actions. Inclusion of indigenous and societal knowledge into the data processing pipelines enables a feedback mechanism that facilitates data driven public services.
... To evaluate it as a tool for supporting suggestions for replicability and reusability of scientific processes, the KB has been connected to the Virtual Earth Laboratory (VLab) for workflow execution. The VLab (Santoro et al., 2020) is an orchestrator framework that automates the technical tasks underpinning the execution of a workflow minimizing the interoperability requirements for both model developers and model users. Through the VLab, modelers can publish a computational model, developed in any of the supported programming environments (e.g., Python, Java, R, NetLogo, etc.), and allow external users to run it and access the results. ...
During the past two centuries, the world has undergone deep societal, political, and economical changes that heavily affected human life. The above changes contributed to an increased awareness about the deep impact that policy decisions have at the local and the global level. Therefore, there is a strong need that policy-making and decision-making processes for a sustainable development be based on the best available knowledge about Earth system and environment.
The recent advance of information technologies enables running complex models that use the large amount of Earth Observation datasets available. However, data and model interoperability are still limited to the syntactic level allowing to access and process datasets independently of their structural characteristics (data format, coordinate reference systems, service interface, …) but with no clear reference to their content (the semantic level) and context of use (the pragmatic level). This poses heavy limitations to the reusability of scientific processes and related workflows.
The paper presents a general framework to address this issue through the design of a Knowledge Base supporting data and model semantic (and pragmatic) interoperability. In this framework, a general ontology represents the knowledge generation process for policy relevant decision-making, while multiple vocabularies formalize the semantics of data and models, identifying different types of observables, process variables, and indicators/indices. To evaluate the proposed approach to semantic interoperability of data and models, the Knowledge Base has been integrated with an advanced model-sharing framework, and a proof-of-concept has been developed for the assessment of one of the indicators of the Sustainable Development Goals defined by the United Nations.
... To fill the above-mentioned gaps, this study aims to make use of multi-cloud and web-services technologies to implement an operational workflow to quantify water stress in Europe from a recently published time series of SEVIRI-ET a and SEVIRI-ET 0 data. Therefore, the main focus is to implement, execute and analyze the advantages and drawbacks of the workflow in an online Virtual Earth Laboratory (VLab) (Santoro, Mazzetti, and Nativi 2020) platform, utilizing Amazon Web Services, as a demonstration, to quantify one-decade (2011-2020) of monthly spatio-temporal water stress variations for the entire European continent. Moreover, to encourage open science, we published the methodology, and all produced results through GitHub (for the workflow code), GeoServer (for the workflow outputs), and GeoNetwork (for documentation of GeoServer layers), enabling it to create a dashboard for better visualization and utilization of results for end-users and (policy) decision-makers. ...
... last access: 23 February 2022). The VLab (Santoro, Mazzetti, and Nativi 2020), inspired by the GEO Model Web vision (Nativi, Mazzetti, and Geller 2013;Santoro, Nativi, and Mazzetti 2016), is developed to facilitate scientific workflows implementation for supporting knowledge generation and evidence-based decision-making. VLab enables: (i) web-based access to heterogeneous resources (e.g. ...
... Such VLab convention files are expected to be found in the model source code repository. All details about VLab architecture and internal functioning are provided in Santoro, Mazzetti, and Nativi (2020). The water stress detection workflow in VLab takes as inputs daily time series of SEVIRI-ET a , SEVIRI-ET 0, and a border of Europe (as a shapefile). ...
Cloud computing facilities can provide crucial computing support for processing the time series of satellite data and exploiting their spatio-temporal information content. However, dedicated efforts are still required to develop workflows, executable on cloud-based platforms, for ingesting the satellite data, performing the targeted processes, and generating the desired products. In this study, an operational workflow is proposed, based on monthly Evaporative Stress Index (ESI) anomaly, and implemented in cloud-based online Virtual Earth Laboratory (VLab) platform, as a demonstration, to monitor European agricultural water stress. To this end, daily time-series of actual and reference evapotranspiration (ETa and ET0), from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor, were used to execute the proposed workflow successfully on VLab. The execution of the workflow resulted in obtaining one decade (2011–2020) of European monthly agricultural water stress maps at 0.04˚ spatial resolution and corresponding stress reports for each country. To support open science, all the workflow outputs are stored in GeoServer, documented in GeoNetwork, and made available through MapStore. This enables creating a dashboard for better visualization of the results for end-users. The results from this study demonstrate the capability of VLab platform for water stress detection from time series of SEVIRI-ET data.
... In future, work, to facilitate the exploitability, interoperability between models and reproducibility of the results, the dasymetric mapping module will be published using the Virtual Earth Laboratory (VLab) framework [47,48]. ...
Local and Regional Authorities require indicators at the intra-urban scale to design adequate policies to foster the achievement of the objectives of Sustainable Development Goal (SDG) 11. Updated high-resolution population density and settlement maps are the basic input products for such indicators and their sub-indicators. When provided at the intra-urban scale, these essential variables can facilitate the extraction of population flows, including both local and regular migrant components. This paper discusses a modification of the dasymetric method implemented in our previous work, aimed at improving the population density estimation. The novelties of our paper include the introduction of building height information and site-specific weight values for population density correction. Based on the proposed improvements, selected indicators/sub-indicators of four SDG 11 targets were updated or newly implemented. The output density map error values are provided in terms of the mean absolute error, root mean square error and mean absolute percentage indicators. The values obtained (i.e., 2.3 and 4.1 people, and 8.6%, respectively) were lower than those of the previous dasymetric method. The findings suggest that the new methodology can provide updated information about population fluxes and processes occurring over the period 2011–2020 in the study site—Bari city in southern Italy.
... A second proof-of-concept was then developed to implement an DT of the Earth instance, following the philosophy and archetype depicted in Figure 2. For the orchestration solution, it makes use of the VLab technology [76]. The two proof-of-concepts will be discussed in a forthcoming manuscript. ...
This manuscript discusses the key characteristics of the Digital Ecosystems (DEs) model, which, we argue, is particularly appropriate for connecting and orchestrating the many heterogeneous and autonomous online systems, infrastructures, and platforms that constitute the bedrock of a digitally transformed society. Big Data and AI systems have enabled the implementation of the Digital Twin paradigm (introduced first in the manufacturing sector) in all the sectors of society. DEs promise to be a flexible and operative framework that allow the development of local, national, and international Digital Twins. In particular, the “Digital Twins of the Earth” may generate the actionable intelligence that is necessary to address global change challenges, facilitate the European Green transition, and contribute to realizing the UN Sustainable Development Goals (SDG) agenda. The case of the Destination Earth initiative and system is discussed in the manuscript as an example to address the broader DE concepts. In respect to the more traditional data and information infrastructural philosophy, DE solutions present important advantages as to flexibility and viability. However, designing and implementing an effective collaborative DE is far more difficult than a traditional digital system. DEs require the definition and the governance of a metasystemic level, which is not necessary for a traditional information system. The manuscript discusses the principles, patterns, and architectural viewpoints characterizing a thriving DE supporting the generation and operation of “Digital Twins of the Earth”. The conclusions present a set of conditions, best practices, and base capabilities for building a knowledge framework, which makes use of the Digital Twin paradigm and the DE approach to support decision makers with the SDG agenda implementation.
