Autonomous GIS uses large language models as its reasoning core to automate spatial data collection, analysis, modeling, and visualization for solving spatial problems with minimal human intervention. Often implemented through autonomous geospatial agents that work independently or collaboratively, it represents a shift from GIS as a passive software environment requiring expert operation toward systems capable of task-oriented interaction, workflow generation, and increasingly autonomous spatial analysis and decision support. However, current GIS agents are often isolated and tightly coupled with specific platforms, tools, or applications, which limits their sharing, reuse, and interoperability across systems and domains. In this paper, we introduce Geospatial Agentic Services, or GAS, as a service-oriented framework for publishing, discovering, invoking, and composing GIS agents as interoperable geospatial services. GAS extends geospatial interoperability beyond access to data and predefined operations toward agentic geospatial workflows, where GIS agents function as discoverable, reusable, and collaborative service entities that encapsulate spatial knowledge and reasoning. Building on service-oriented architecture and the A2A protocol, GAS supports structured geospatial skill declarations, agentic service registry and discovery, and distributed agent orchestration. It also integrates validation, provenance, reproducibility, and governance to support transparent and trustworthy geospatial analyses. To demonstrate the framework, we prototyped a GAS platform with six GIS agents and four orchestrated workflows. By connecting traditional geospatial service architecture with emerging agentic interoperability, GAS provides a conceptual and architectural foundation for interoperable, reusable, and scalable agentic geospatial services and workflows in the era of Autonomous GIS.

For 30 years, the Open Geospatial Consortium (OGC) has advanced geospatial interoperability through continuous innovation. The OGC provides strategies and Standards that promote adoption, increase efficiencies, create new opportunities, and transform our relationship with the dynamic planet we inhabit. It has fostered global collaborations among companies, governments, academic institutions, and non-profit organizations, reducing the friction inherent in introducing innovations to the industry. The article explores the OGC’s history and future aspirations, demonstrating how it became a global community of innovators dedicated to ensuring their geospatial tools and data can work together seamlessly, supporting both essential public missions and private enterprises. The OGC brand is renowned not only for its technical innovation pilots but also for its leadership in global standardization within the geospatial technology community. Additionally, the article also highlights the OGC's commitment to enhancing reproducibility through research and development in data representation, discovery, and access as part of its standardization and research activities. The OGC is committed to promoting connectivity between people, technology, and decision-making, leveraging FAIR (Findable, Accessible, Interoperable, Reusable) principles.

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?

Standard data models are powerful to enable data interoperability. However, due to their ambition to comprehensively represent a domain, they tend to be over-specified. On the other hand, they allow too much flexibility in implementation and modelling details, to adapt to several use cases needs. The OGC Data Exchange Toolkit intends to overcome the two issues by means of a methodology to define profiles through linked data technologies, operated via the OGC Rainbow. Moreover, attributes are defined to specify the desired object representation requirements, solving the under-specification issue. A template is provided, intended to support manual input of data requirements, generating in turn a human-readable description and amachine-readable input for the profiling tool itself. The profile allows automatic datasets validation against the defined requirements. The prototype is developed and tested on a sample CityJSON dataset.

Data spaces are conceptualised as a trusted and secure distributed data ecosystem through which to exchange resources in the Web. Several efforts define guidance toward data space implementation, such as reference architectures and frameworks. As yet, the proposed data space solutions do not provide common and mature implementation options yet, and this gap between concept and implementation risks confusing users and developers. However, well-recognised organisations have been developing solutions and standards that address interoperability and good data exchange practices for decades, especially in the domain of geospatial information and remote sensing. Therefore, this paper compares the available solutions, providing the mapping and integration of the proposed blueprints to available interoperable standards. This concrete mapping, followed by a discussion with experts, results in a proposal of integrated reference data space building blocks, and an overview of the related standards and solutions. It is designed to support the effective practical implementation of data spaces and to guide future solution developments. This work can form the base for effective collaboration among different organisations, clearly identifying their scopes. A key role is apparent for standards and use cases from the remote sensing and geospatial domains, which have achieved wide adoption and maturity over the past years.

Standard data models are key to enable a set of data integration functionalities, often characterised using the Findability, Accessibility, Interoperabilty and Reusability (FAIR) principles. However, standardisation is a process of trying to meet many requirements, and standard data models are inherently either very abstract or very comprehensive in the details. This results in several ambiguity pitfalls, inconsistent implementation of standard data models, which in turn hinders trust in the interoperability potential of standardised data, and complicates any integration processes. In practice profiling such standards is useful to overcome such issues to create more useful forms of standardised data for specific applications. However defining custom profiles typically requires a great deal of technical expertise in the underlying expression language of the standard. Maintaining access to this level of expertise is a challenge as profiles become outdated through the time and lose connection with the maintenance of the parent standard from which they originate. Therefore, in this paper, a scalable methodology is proposed, built on the OGC Building Blocks Model approach, that uses semantic modelling to support an easier composition of geospatial data models profiles which directly derive from available standards without losing the relevant dependencies that inform stakeholders which components are interoperable with other standards. The approach is tested within a digital building permit project (CHEK), in which data requirements derive from the semantics of city regulations and common geospatial standards (i.e., CityGML and INSPIRE) are used as reference.

A number of policy initiatives over the last three decades have been striving to limit the impacts of global warming, reverse land degradation, stop the loss of biodiversity, protect finite natural resources, and reduce the risks associated with environmental disasters. They are summarized under the umbrella of the United Nations Agenda 2030 and the Sustainable Development Goals (SDGs), which together aim at building the resilience of people and systems to new environmental and socioeconomic conditions in the future.Beyond political will, achieving these objectives and tracking progress requires science, data and technology: sophisticated observation instruments and networks, coordinated modeling experiments, scientific assessments, global data access infrastructures and analytical capacity to process and translate data into relevant information. While some of these endeavors have benefited from international coordination and funding under the umbrella of the UN 2030 Agenda for Sustainable Development, the Paris Agreement on climate change, the Sendai Framework for Disaster Risk Reduction and other framework agreements, many still rely on uncoordinated national or international projects, resulting in a proliferation of incompatible, short-lived initiatives. Beyond the waste of time and energy, our main concern is that many solo technological initiatives all solve the same simple problems, deferring work on more complex issues.Advances on the hard global problems of our time requires greater collaboration and innovation. We argue that policy instruments should include technological guidelines, for example mandate the use of international standards for analysis ready data formats, metadata, geodatacubes or machine to machine communication protocols, in order to foster interoperability and software reuse. This would strengthen international collaboration on innovative development, and in turn contribute to the deployment of effective, robust and scientifically credible products to support decision-making and local climate action.

In May 2007, the INSPIRE directive established the path towards creating the European Spatial Data Infrastructure (ESDI). While the Joint Research Centre (JRC) defined a set of detailed implementation guidelines, the European member states determined the agencies responsible for delivering the different topics specified in the directive’s annexes. INSPIRE’s goal was - and still is - to organize and share Europe’s data supporting environmental policies and actions. However, the way that INSPIRE was defined limited contributions to the public sector, and limited topics to those specifically listed in its annexes. Technical challenges and a lack of appropriate tools have impeded INSPIRE from implementing its own guidelines, and even after 15 years, the dream of a continuous, consistent description of Europe’s environment has still not completely materialized. We should apply the lessons learnt in INSPIRE when we build the Green Deal Data Space (GDDS). To create the GDDS, we should start with ESDI (the European Spatial Data Infrastructure), but also engage and align with the ongoing preparatory actions for data spaces (e.g., for green deal and agriculture) as well as include actors and networks that have emerged or been organized in the recent years. These include: networks of in situ observations (e.g. the Environmental Research Infrastructures (ENVRI) community); Citizen Science initiatives (such as the biodiversity observations integrated in the Global Biodiversity Information Facility (GBIF), or sensor communities for e.g. air quality); predictive algorithms and machine learning models and simulations based on artificial intelligence (such as the ones deployed in the European Open Science Cloud, International Data Space Association and Gaia-X; services driven both by the scientific community and the private sector); remote sensing derived products developed by the Copernicus Services. Most of these data providers have already embraced the FAIR principles and open data, providing many examples of best practice which can assist newer adopters on the path to open science. In the Horizon Europe project AD4GD (AllData4GreenDeal), we believe that, instead of trying to force data producers to adopt cumbersome new protocols, we should take advantage of the latest developments in geospatial standards and APIs. These allow loosely coupled but well documented and interlinked data sources and models in the GDDS while achieving scientifically robust integration and easy access to data in the resulting workflows. Another fundamental element will be the adoption of a common and extensible information model enabling the representation and exchange of Green Deal related data in an unambiguous manner, including vocabularies for Essential Variables to organize the observable measurements and increase the level of semantic interoperability. This will allow systems and components from different technology providers to seamless interoperate and exchange data, and to have an integrated view and access to exploit the full value of the available data. The project will validate the approach in three pilot cases: water quality and availability of Berlin lakes, biodiversity corridors in the metropolitan area of Barcelona and low cost air quality sensors in Europe. The AD4GD project is funded by the European Union under the Horizon Europe program.

Over the past decade, a multitude of independent initiatives have developed solutions to answer the need for Analysis-Ready Data, in order to reduce the time and effort required in order to generate added value out of raw, heterogeneous data. These initiatives resulted in various GeoDataCube (GDCs) implementations, standards, data formats and best practices.Interoperability between the GDC solutions has so far not been a core concern but with the increasing amount of data served as GDCs as well as its increasing uptake, it is becoming essential to understand what exactly a GDC entails, how it was created, and how different GDCs can be used together consistently. This presentation shall focus on the OGC GeoDataCube Standards Working Group (SWG) which has recently been initiated to tackle these issues proposing viable solutions defined as a standard.

Abstract. The concepts of smart cities and digital twins are more and more investigated and accepted as critical tools for the cities of the future. In order to make them a concrete reality, several aspects related to the management of data have to be considered: technical issues to collect, retrieve, exchange, analyse and process data; data sovereignty issues; data semantics, features and metadata specifications. In current projects and higher level frameworks specifications, some of these issues are explored and solutions are proposed for subset of them. However, an overall framework connecting those aspects in a unique model has not been defined and tested yet. In this paper, we propose a reference model to build an interoperable system of systems supporting the implementation of smart cities and digital twins. We start from reviewing current experiences and in particular considering the OGC standards and initiatives intended to provide Open solutions for specific parts of the framework.

The e-shape H2020 Project brings together decades of public investment in earth observation and in cloud capabilities into services to the citizens, the industry, the decision-makers and the researchers. e-shape promotes the development and uptake of a number of cloud-based pilot applications addressing the Sustainable Development Goals, The Paris Agreement and the Sendaï Framework. The consortium gathers 55 members from 17 European countries, Ethiopia, Egypt and Israel. It is a European contribution to GEOSS [1]. A major deliverable at the end of the project in 2023, will be a Guide for European Earth Observation application developers, decision-makers, and experts delivering best practices to use Earth Observation resources based on the experience collected during the project. This guide will provide a unique source and guidelines to increase the usage and exploitation of Earth Observation in the thematic domains addressed by e-shape.

Ongoing climate change is increasingly impacting ecosystems and living conditions. To understand climate change effects on all scales ranging from regional to global and to develop appropriate response strategies, reliable, easily accessible climate location information is crucial. The United Nations framework of climate change policy emphasizes the role of open data as an essential component to enable efficient implementation of appropriate climate change strategies. Data offered at the various portals and climate services needs to be Findable, Accessible, Interoperable, and Reusable (FAIR). This is particularly important when several communities need to work together in order to develop the most effective response strategies. These communities not only involve climate scientists and meteorologists, but also climate impact analysts, hydrologists, agronomists, urban planners, ecologists, and many more. Screening the web for available data, it becomes apparent that there is no shortage of portal solutions built upon climate data archives. Portal solutions have turned out in the past of often being targeted towards a specific, and sometimes rather small, number of users from within a single community. Cross-community integration and thus enhanced reusability and interoperability was not in focus. Due to recent ongoing international domain crossing efforts, FAIR principles are increasingly respected also for the portal architectures of the information systems itself. For example, the Open Geospatial Consortium (OGC) develops standards and best practices that enable FAIR principles across communities.FAIR principles across communities require a set of essential ingredients to work effectively. These ingredients include metadata models that allow discovery (Findable), interfaces to access the data (Accessible), data models that are well documented (Interoperable) and can be efficiently consumed by others (Reusable). Because data volumes are continuously growing and therefore require new approaches for efficient data processing, OGC has extended the ‘Reusable’ component in FAIR. ‘Reusable’ now includes mechanisms for executing applications close to the physical location of the data. What was previously a data provisioning system now needs to be extended to support processing capacities up to the level where user-defined applications can be deployed and executed. In a sense, for data to be FAIR, it needs to be accompanied by equally FAIR services. This presentation is showing current realisations of leading climate services information systems that implement the extended FAIR principle. The presentation will sort out roles and capabilities of standardized web APIs that can be assembled in line with data and processing environments for interoperable climate data across communities in the most efficient way. Once paired with OGC’s new “Applications-to-the-Data” architecture and strong metadata models, the web APIs enable effective integration of climate data with data from other disciplines within state-of-the art cloud environments that feature not only reusability of data, but also of applications, data processes, and scientific workflows.

. The OGC Body of Knowledge is a structured collection of concepts and related resources that can be found in the set of documents published by the Open Geospatial Consortium (OGC). An explicit view of this knowledge is available from the OGC Virtual Knowledge Store and related components such as the OGC Definitions Server and the OGC Glossary of Terms. The OGC Body of Knowledge is intended to provide a reference for users and developers of geospatial software and services. This paper describes the approach taken to develop the OGC Body of Knowledge and presents the results of the approach. It is intended to encourage and facilitate discussion within the OGC membership and wider geospatial community.

In the digital bio-economy like in many other sectors, standards play an important role. With “Standards”, we refer here to the protocols that describe how data and the data-exchange are defined to enable digital exchange of data between devices. This chapter evaluates how Big Data, cloud processing, and app stores together form a new market that allows exploiting the full potential of geospatial data. This chapter focuses on the essential cornerstones that help make Big Data processing a more seamless experience for bioeconomy data. The described approach is domain-independent, thus can be applied to agriculture, fisheries, and forestry as well as earth observation sciences, climate change research, or disaster management. This flexibility is essential when it comes to addressing real world complexities for any domain, as no single domain has sufficient data available within its own limits to tackle the major research challenges our world is facing.

Exploitation unleashes the value of Earth Observations. OGC, IEEE GRSS, and other communities work in coordination to define standards and community practices for advancing EO Exploitation. This paper provides a summary of OGC activities in the development of platforms, applications and open standards for EO Exploitation. A key example is the use of OGC exploitation platform for application of Machine Learning to EO Data.

The advancements in Big data processing pipelines have consolidated the "processes to the cloud" paradigm over the last decade. This represents a shift away from downloads and local processing. Recent publications have addressed commercialization elements such as quoting and billing across clouds and predict the development of markets pretty similar to application markets we know from the mobile phone sector. At the same time, Analysis Ready Data becomes available at many places. This paper addresses the question how to discover all these applications, Analysis Ready Data, and cloud resources in an interoperable way, meaning that the process is not different from resource to resource.

Big Data processing chains working on geospatial data such satellite imagery have been subject of research for many years and are now being operationalized in standardized ways. Well-defined interfaces and standardized data exchange formats have been developed that make working with satellite imagery end-user friendly. Recent advances in the standardization domain even support the ’applications to the data’ paradigm that allows executing arbitrary applications close to the physical location of the data. What was missing so far was a smooth integration of commercial principles, such as solid authentication and authorization, as well as quotation and billing mechanisms. This paper outlines how the integration of those commercial key elements can happen.