{"id":2277,"date":"2023-01-10T12:01:00","date_gmt":"2023-01-10T12:01:00","guid":{"rendered":"https:\/\/fm-connect1.fortmindz.in\/wp-ogc\/?post_type=initiatives&#038;p=2277"},"modified":"2025-11-26T11:13:28","modified_gmt":"2025-11-26T11:13:28","slug":"t17","status":"publish","type":"initiatives","link":"https:\/\/fm-connect1.fortmindz.in\/wp-ogc\/initiatives\/t17\/","title":{"rendered":"OGC Testbed-17"},"content":{"rendered":"\n<p>The Open Geospatial Consortium (OGC) has published the outcomes of 2021\u2019s biggest research and development initiative, Testbed-17. The key outcomes, including detailed Engineering Reports,&nbsp;are freely available on the <a href=\"http:\/\/docs.opengeospatial.org\/per\/\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">OGC Public Engineering Reports&nbsp;webpage<\/a>. The following list provides an overview of all topics. If you like to get a first overview, please feel free to download the <a href=\"https:\/\/portal.ogc.org\/files\/?artifact_id=99603\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">recording of the&nbsp;Testbed-17 results session<\/a>&nbsp;given at the OGC Member Meeting in December 2021. Testbed-17 was organized into three thematic \u2018Threads\u2019 that conducted Research &amp; Development (R&amp;D) on the following cutting-edge geospatial technologies:<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Thread 1: Advanced sensor integration for moving and static objects&nbsp;<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Sensor Integration<\/strong><br>\n<ul class=\"wp-block-list\">\n<li><strong>Sensor Integration Framework Assessment Engineering Report<\/strong>:\u00a0This task focused on the integration of sensor systems regardless of their technical constraints and deployment environment. This was demonstrated implementing concepts described in the Sensor Integration Framework (SIF) standard developed by National System for Geospatial Intelligence (NSG) and United States MASINT System (USMS). A secondary objective was to demonstrate the possibility of integrating an OGC SensorThings API server with an existing SIF implementation called MASBUS. The developed implementations target systems that could be deployed on enterprise networks as well as in Denied, Degraded, Intermittent, or Limited Bandwidth (DDIL) environments. <a href=\"http:\/\/docs.opengeospatial.org\/per\/21-022.html\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">OGC Testbed-17: Sensor Integration Framework Assessment Engineering Report<\/a><br><br><\/li>\n\n\n\n<li><strong>SIF Semantic Model Engineering Report<\/strong>:\u00a0A significant barrier to sensor integration is often the variety of standards, formats, and protocols employed in sensor systems. To build impactful sensor systems, it is necessary for such systems to embrace this diversity. There is, therefore, a need for a framework of standards that facilitates sensor integration independently of technological restrictions. This task analysed the semantic aspects of the Sensor Integration Framework (SIF). <a href=\"http:\/\/docs.opengeospatial.org\/per\/21-030.html\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">OGC Testbed-17: SIF Semantic Model Engineering Report<\/a><br><br><\/li>\n\n\n\n<li><strong>MASBUS Integration Engineering Report<\/strong>:\u00a0This Testbed-17 MASBUS Integration Engineering Report (ER) describes how interoperability can be established in a common scenario of heterogeneous data sources (e.g. sensors, IoT platforms, simulators and other data models and encodings). Moreover, the architecture presented in this ER allows querying and visualizing observations from data sources using widely adopted international standards such as the OGC SensorThings API (<a href=\"http:\/\/docs.opengeospatial.org\/per\/21-029.html#OGC_19-088\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">OGC 19-088<\/a>). <a href=\"http:\/\/docs.opengeospatial.org\/per\/21-029.html\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">OGC Testbed 17: MASBUS Integration Engineering Report<\/a><br><br><\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Moving Features from Full Motion Imagery<\/strong>:<br>\n<ul class=\"wp-block-list\">\n<li><strong>Moving Features Engineering Report<\/strong>: Moving Features play an essential role in many application scenarios. The growing availability of digital motion imagery and advancements in machine learning technology will further accelerate widespread use and deployment of moving feature detection and analysis systems. The OGC Testbed-17 Moving Features task considers these developments by addressing exchange of moving object detections, shared processing of detections for correlation and analysis, and visualization of moving objects within common operational pictures. This OGC Moving Features Engineering Report explores and develops an architecture for collaborative distributed object detection and analysis of multi-source motion imagery. <a href=\"http:\/\/docs.opengeospatial.org\/per\/21-036.html\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">OGC Testbed-17: Moving Features Engineering Report<\/a><br><br><\/li>\n\n\n\n<li><strong>Moving Features API Engineering Report<\/strong>:The goal is to define a powerful Application Programming Interface (API) for discovery, access, and exchange of moving features and their corresponding tracks and to exercise this API in a near real-time scenario. <a href=\"http:\/\/docs.opengeospatial.org\/per\/21-028.html\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">OGC Testbed-17: OGC API &#8211; Moving Features Engineering Report<\/a><br><br><\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>OGC API-Aviation<\/strong>:&nbsp;The System Wide Information Management (SWIM) Data Services of the Federal Aviation Administration (FAA) produce data from the National Airspace System (NAS) to consumers. SWIM services use various protocols and service offerings in both synchronous and asynchronous messaging formats. OGC has addressed the multi-format and multi-model interoperability challenges in past testbeds. This Engineering Report documents the findings applying OGC API design and principles to SWIM data services. It discusses OpenAPI-based Web APIs for SWIM, components to fuse data, and interoperability challenges in multi-stakeholder environments.<a href=\"http:\/\/docs.opengeospatial.org\/per\/21-039r1.html\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"> OGC&nbsp;Testbed-17: Aviation API Engineering Report<\/a><\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Thread 2: Geospatial Data Clouds and Model Driven Standards<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Model Driven Standards<\/strong><br><br>\n<ul class=\"wp-block-list\">\n<li><strong>Model Driven Standards Engineering Report<\/strong>: The Model-Driven Architecture (MDA) approach to the generation of Model-Driven Standards (MDS) and Platform-Independent Models (PIM) has been proven to work. Various prototypes have been developed and proven that an MDA approach, possibly an organizational-wide one, would streamline the downstream creation of MDSes and PIMs, and greatly benefit all stakeholders of the model-driven process. Specifically, the usage of a single source of truth across all MDA elements will guarantee a certain consistency, and also provide upstream feedback to conceptual model authors on potential impact of seemingly inconsequential changes. The strong implication for using an MDA approach is that generally, the MDA approach makes any inconsistencies, omissions, or under-specifications in underlying information models much more visible, since they impact the MDA workflow directly. <a href=\"http:\/\/docs.opengeospatial.org\/per\/21-035r1.html\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">OGC Testbed-17: Model-Driven Standards Engineering Report<\/a><br><br><\/li>\n\n\n\n<li><strong>UML Modeling Best Practices Engineering Report<\/strong>:\u00a0This OGC Best Practice provides readers with guidance on how to use the Unified Modeling Language (UML) within the scope of OGC work. Recently there has been a move to a resource-based approach for OGC Application Programming Interface (API) definition through the OpenAPI Specification and away from the service-based approach specified in OGC Web Service (OWS) standards. Previously, the interface definitions were almost exclusively XML based, therefore models described using UML class diagrams and conceptual models in general simply mapped 1:1 to derive the XML schema. Using API resources has resulted in the possibility of deriving multiple target technologies from a single standard and therefore, UML model. An additional point of discussion within the OGC is the value added by conceptual modeling using UML. Models included in OGC Standards vary from diagrams only, to conceptual models and model fragments all the way through to Model Driven Architecture (MDA) where UML models are used to directly derive implementable artifacts such as schemas. <a href=\"http:\/\/docs.opengeospatial.org\/per\/21-031.html\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">OGC Testbed-17: UML Modeling Best Practice Engineering Report<\/a><br><br><\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>COG &amp; Zarr Specification &amp; Evaluation<\/strong><br><br>\n<ul class=\"wp-block-list\">\n<li><strong>COG &amp; ZARR Evaluation<\/strong>:\u00a0The focus of this is ER is documenting experiments in working with geospatial data in cloud-based environments. Specifically, the possibility of using two specific formats dedicated to managing the storage and distribution of images and data: Cloud Optimized GeoTIFF (COG) and Zarr. The evaluation of the use of these two data structures is carried out in two independent contexts: the first one (named \u201cCommercial Applications\u201d) is based on the use of the current implementations of COG and Zarr within existing geospatial applications. The second one is related to the implementation work \u00a0completed in Testbed-17. The differentiation helps to obtain an overview of the state of the art, the current development directions, and any future work. <a href=\"http:\/\/docs.opengeospatial.org\/per\/21-032.html\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">OGC Testbed 17: COG\/Zarr Evaluation Engineering Report<\/a><br><br><\/li>\n\n\n\n<li><strong>Cloud Optimized GeoTIFF Specification<\/strong>: Cloud Optimized GeoTIFF (COG) is a new approach in using existing standards to accelerate distribution and analysis of 2D regular grid coverage data on the web. COG combines the use of the TIFF format with data structured internally in tiles and low resolutions subfiles (also called overviews). The main subfile is georeferenced using GeoTIFF tags and the lower resolution subfiles inherit the same georeferencing. This organization allows for retrieving only the part of the data needed for presentation or analysis. This capability is possible not only in the file system but also over the web if the HTTP range header is supported by the servers. This OGC Testbed 17 Engineering Report (ER) discusses the COG approach, describes how GeoTIFF is used for the lower resolution subfiles, and proposes a different path forward that integrates COG with the OGC Tile Matrix Set Standard. <a href=\"http:\/\/docs.opengeospatial.org\/per\/21-025.html\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">OGC Testbed-17: Cloud Optimized GeoTIFF specification Engineering Report<\/a><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Thread 3: Interoperability through APIs<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Data Centric Security<\/strong>:\u00a0Data Centric Security (DCS) is an approach to apply security directly to data, independent from security features provided by a network, servers or applications. For Data Centric Security in the geospatial domain, proof-of-concept implementations were developed through work in OGC Testbed-15 and Testbed-16. Initially Extensible Markup Language (XML) based standards to label and protect geospatial feature data in Testbed-15 according to the NATO STANAG 4774 and 4778 specifications, the work expanded into JavaScript Object Notation (JSON) based structures during Testbed-16. For this Testbed-17, the primary goal of the DCS task was to apply Data Centric Security in the context of OGC API Standards that enable the delivery of binary data representations such as images and GeoPackage. All results are published in <a href=\"http:\/\/docs.opengeospatial.org\/per\/21-020r1.html\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">OGC Testbed-17: Data Centric Security\u00a0Engineering Report<\/a><br><br><\/li>\n\n\n\n<li><strong>Models for Real World Objects and OGC APIs<\/strong><br><br>\n<ul class=\"wp-block-list\">\n<li><strong>OGC Features and Geometries JSON<\/strong>:\u00a0JavaScript Object Notation (JSON) is a popular encoding format for geospatial data. The lightweight, simple syntax, and clear human and machine readability of JSON appeals to developers. GeoJSON has become a very popular encoding and is supported in most deployments of APIs implementing OGC API Features. However, GeoJSON has limitations that prevent or limit its use in some cases, e.g., if other coordinates should be in a projected coordinate reference system. To support additional use cases, in 2021 the OGC formed a new Standards Working Group (SWG) to develop an OGC Features and Geometries JSON standard (JSON-FG). The OGC Testbed-17 Features and Geometries JSON task investigated proposals for how feature data could be encoded in JSON so that different Coordinate Reference Systems (CRS) are supported. Further on, Testbed-17 explored mechanisms that allow communities to build and formally specify profiles of the fully CRS-enabled JSON with limited sets of supported geometry types and with clear constraints for feature type definitions. <a href=\"http:\/\/docs.opengeospatial.org\/per\/21-017r1.html\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">OGC Testbed-17: OGC Features and Geometries JSON Engineering Report<\/a><br><br><\/li>\n\n\n\n<li><strong>Features and Geometries JSON CRS Analysis of Alternatives<\/strong>:\u00a0One of the primary requirements for the OGC Testbed-17 Features and Geometries JSON task is to define an extension or profile of GeoJSON that supports encoding spatiotemporal data in Coordinate Reference Systems (CRS) other than the GeoJSON default of the World Geodetic System 1984 (WGS 84) datum, with longitude and latitude units of decimal degrees (CRS84). This OGC Testbed-17 Engineering Report \u00a0presents the various alternatives considered for declaring CRS information in a Features and Geometries JSON (JSON-FG) file. JSON-FG is an OGC extension to GeoJSON that, among other things, adds support of coordinate reference systems other than the CRS84 default. One of the alternatives was selected to be the mechanism for declaring CRS information in a JSON-FG document and is fully described in the \u201cOGC Testbed-17: OGC Features and Geometries JSON Engineering Report\u201d (OGC 21-017r1), see above. <a href=\"http:\/\/docs.opengeospatial.org\/per\/21-018.html\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">OGC Testbed-17: Features and Geometries JSON CRS Analysis of Alternatives Engineering Report<\/a><br><br><\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Attracting Developers: Lowering the entry barrier for implementing OGC Web APIs<\/strong>:\u00a0OGC Web API Standards are being developed to make it easy to provide geospatial data over the web. These standards provide a certain level of formality to ensure high levels of interoperability. They rigorously define requirements and rules to reduce room for error during interpretation. This rigor sometimes makes the standard documents difficult to read and hence implement. Rather than direct examination of a standard, the majority of developers often prefer to start with implementation guidelines, sample code, and best practice documentation and then refer to the standards document for guidance and clarity. The Testbed-17 API task served as a foundation for further development and exploration and delivers knowledge necessary for agile development, deployment, and executing OGC Standards-based applications following a \u201cHow-To\u201d philosophy with hands-on experiments, examples, and instructions. <a href=\"http:\/\/docs.opengeospatial.org\/per\/21-019.html\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">OGC Testbed-17: Attracting Developers: Lowering the entry barrier for implementing OGC Web APIs<\/a><br><br><\/li>\n\n\n\n<li><strong>Geo Data Cubes<\/strong>:\u00a0This Engieneering Report defines a draft specification for an interoperable Geo Data Cube (GDC) API leveraging OGC API building blocks, details implementation of the draft API, and explores various aspects including data retrieval and discovery, cloud computing and Machine Learning. Implementations of the draft GDC API are demonstrated with use cases including the integration of terrestrial and marine elevation data and forestry information for Canadian wetlands. <a href=\"http:\/\/docs.opengeospatial.org\/per\/21-027.html\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">OGC Testbed 17: Geo Data Cube API Engineering Report<\/a><br><br><\/li>\n\n\n\n<li><strong>Compliance Interoperability, Testing &amp; Evaluation<\/strong>:&nbsp;This ER provides information about the development of a test suite for the OGC API\u2009\u2014\u2009Processes Standard (<a href=\"https:\/\/docs.ogc.org\/is\/18-062r2\/18-062r2.html\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">OGC 18-062r2<\/a>) to be executed in the OGC Test Evaluation tool (TEAM Engine). The ER also documents an evaluation of an alternative environment for OGC compliance testing. <a href=\"http:\/\/docs.opengeospatial.org\/per\/21-044.html\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">OGC Testbed 17: CITE Engineering Report<\/a><\/li>\n<\/ul>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/portal.ogc.org\/files\/?artifact_id=100753\" alt=\"\"\/><\/figure>\n\n\n\n<p>All&nbsp;16 Engineering Reports&nbsp;outlining the findings of the year-long research and development conducted by the Participants are now published and <a href=\"http:\/\/docs.opengeospatial.org\/per\/\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">freely available<\/a> to the public in both html and pdf format.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Background<\/strong><\/h2>\n\n\n\n<p>In a collaborative effort, all Testbed participants, sponsors, and the OGC team work together to stepwise increase Technology Readiness Levels (TRL) for geospatial IT solutions, including software architecture, interface design, information &amp; data models, and related standards and specifications. Testbed participants follow a rapid prototyping approach to design, develop, and test solutions to sponsors\u2019 location-related problems.<\/p>\n\n\n\n<p>OGC\u2019s annual Testbeds are the Consortium\u2019s largest Innovation Program initiatives. Testbeds boost research and development to make location data and information more FAIR: Findable, Accessible, Interoperable, and Re-usable. Testbeds provide a unique opportunity for sponsors to tackle location data and processing challenges together with the world\u2019s leading geospatial IT experts. Solutions developed in OGC Testbeds have gone on to form standards and technologies that now play critical roles in numerous domains across the world.&nbsp;<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><\/li>\n<\/ul>\n\n\n\n<p>To learn about the benefits of sponsoring any OGC IP Initiative in general, visit the <a href=\"https:\/\/portal.ogc.org\/To learn about the benefits of sponsoring any OGC IP Initiative in general, visit the OGC Innovation Program webpage.\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">OGC Innovation Program webpage<\/a>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Call for Participation<\/strong><\/h2>\n\n\n\n<p>Responses to the Call for Participation (CFP) were due in January. Copies of the CFP are available in <a href=\"https:\/\/portal.ogc.org\/files\/?artifact_id=95726\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">HTML format<\/a> and in <a href=\"https:\/\/portal.ogc.org\/files\/?artifact_id=95727\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">PDF format<\/a>.<\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>OGC Testbed-17 advances geospatial R&#038;D with new work on sensors, cloud data models, APIs, and interoperability standards.<\/p>\n","protected":false},"featured_media":2696,"parent":0,"template":"","meta":{"_acf_changed":true,"_eb_attr":""},"initiative-category":[67,43],"initiative-tag":[48,163],"class_list":["post-2277","initiatives","type-initiatives","status-publish","has-post-thumbnail","hentry","initiative-category-completed","initiative-category-initiative","initiative-tag-testbed","initiative-tag-testbed-17"],"acf":[],"_links":{"self":[{"href":"https:\/\/fm-connect1.fortmindz.in\/wp-ogc\/wp-json\/wp\/v2\/initiatives\/2277","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/fm-connect1.fortmindz.in\/wp-ogc\/wp-json\/wp\/v2\/initiatives"}],"about":[{"href":"https:\/\/fm-connect1.fortmindz.in\/wp-ogc\/wp-json\/wp\/v2\/types\/initiatives"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/fm-connect1.fortmindz.in\/wp-ogc\/wp-json\/wp\/v2\/media\/2696"}],"wp:attachment":[{"href":"https:\/\/fm-connect1.fortmindz.in\/wp-ogc\/wp-json\/wp\/v2\/media?parent=2277"}],"wp:term":[{"taxonomy":"initiative-category","embeddable":true,"href":"https:\/\/fm-connect1.fortmindz.in\/wp-ogc\/wp-json\/wp\/v2\/initiative-category?post=2277"},{"taxonomy":"initiative-tag","embeddable":true,"href":"https:\/\/fm-connect1.fortmindz.in\/wp-ogc\/wp-json\/wp\/v2\/initiative-tag?post=2277"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}