OGC Requests

The Open Geospatial Consortium (OGC) seeks public comment on version 1.1 of the OGC GeoPackage Extension for Tiled Gridded Coverage Data (TGCE). 

TGCE defines how to encode and store tiled regular gridded data, such as a digital elevation model, in a GeoPackage. The tiles contain values, such as elevation, temperature or pressure, and the extension defines two encodings: PNG and TIFF. 

GeoPackage is an open, standards-based, platform-independent, portable, self-describing, compact format for transferring geospatial information. For a primer on the capabilities of GeoPackage, read the blog post: #GeoPackageDay 2020 - what is GeoPackage?

There are two substantive changes incorporated into the GeoPackage Tiled Gridded Coverage Version 1.1 extension. These changes do not impact backwards compatibility.

• Support for additional data types in GeoPackage coverages, specifically: 8/16/32 bit signed integers; 8 bit unsigned integer (16-bit integer is currently supported, but only for PNG); 32-bit floating point (already supported in GeoPackage coverages); and 1 bit (e.g. as a binary mask).
• Multiple channels in a GeoPackage Coverage can now be specified.

The release notes containing the full list of changes seen in version 1.1 are available at: portal.ogc.org/files/98334.

The Open Geospatial Consortium (OGC) seeks public comment on the draft Zarr Storage Specification 2.0 Community Standard. 

Zarr is an open-source specification for the storage of multi-dimensional arrays of data (also known as data cubes, N-dimensional arrays, ND-arrays, or tensors). Such arrays are ubiquitous in scientific research and engineering. 

Zarr stores metadata using .json text files and array data as (optionally) compressed binary chunks. Zarr can store data into most storage systems, including databases, standard ‘directory based’ file systems, and cloud object stores, such as Amazon S3. This flexibility allows implementations to experiment with novel storage technologies while maintaining a uniform API for downstream libraries and users.

Zarr arose in genomics research in 2016. It was created by Alistair Miles of Oxford University as a library optimized for massively parallel array analytics. It has since grown into a community project with a range of developers and users from fields such as genomics, bioimaging, astronomy, physics, quantitative finance, oceanography, atmospheric science, climate science, and geospatial imaging. 

Because it can represent very large array datasets in a simple, scalable way, and is compatible with cloud object storage, Zarr is an ideal format for analysis-ready geospatial data in the cloud. Indeed, Zarr has already been adopted by several OGC communities as a format for cloud-optimized, analysis-ready geospatial data. Examples include:

• Climate Science: The CMIP6 Google Cloud Public Dataset
• Oceanography: The ECCOv4r3 Ocean State Estimate
• Atmospheric Science: Global cloud-resolving aquaplanet simulations with the System for Atmospheric Modeling

While Zarr is not inherently a geospatial-specific format, it has been put forward by the Zarr Steering Council for adoption as an OGC community standard because of its rapid growth and adoption in geospatial and related fields.

An approved OGC Community Standard is an official standard of OGC that is considered to be a widely used, mature specification, but was developed outside of OGC’s standards development and approval process. The originator of the standard brings to OGC a “snapshot” of their work that is then endorsed by OGC membership so that it can become part of the OGC Standards Baseline.

The Open Geospatial Consortium (OGC) seeks public comment on version 1.2 of the OGC Indexed 3d Scene Layer (I3S) and Scene Layer Package (*.slpk) Format Community Standard. 

I3S enables the streaming and storage of arbitrarily large amounts of 3D geographic data. An I3S dataset, referred to as a Scene Layer, can consist of millions of discrete 3D objects with attributes, integrated surface meshes, symbolized points, or point cloud data covering vast geographic areas. Designed for performance and scalability, a scene layer enables the efficient encoding and transmission of geospatial content for an interactive visualization experience on web browsers, mobile, and desktop apps for both offline and online access. 

I3S is web and cloud friendly and is rooted in modern standards and technological advancements in the areas of 3D graphics, data structuring, and mesh and texture compression. I3S is accessible for efficient client-side filtering. I3S empowers city and local governments, content providers, and GIS professionals to share scene layers consisting of terabytes of nation-wide point cloud coverage, city and site scale 3D mesh data collected at frequent intervals, 3D planning scenarios fusing both ‘as built’ and modeled information, and more. The increased accessibility and ease of consumption of geospatial content by both domain experts as well as casual users is being rapidly accelerated by standards such as I3S.

The changes included in v1.2 of the OGC I3S Community Standard include:

• Enhanced performance and scalability.
• Introduction of paged node access pattern which significantly reduces the client-server traffic by bundling individual node metadata resources into compact pages of nodes.
• Support for Draco geometry compression. Draco compression of I3S geometry attributes creates more compact nodes, which in turn provides a smaller payload, increasing performance.
• Support for Basis Universal Texture format in Khronos KTX2 standard.
• More optimal selection strategy – standardizes on Oriented Bounding Boxes (OBBs) based node selection criterion.
• Support for advanced material definitions such as physically based materials. Feature compatible with Khronos glTF standard.
• Numerous editorial updates/corrections.

This version 1.2 of the OGC Community Standard mirrors version 1.7 of the original Esri Scene Layers: Service and Package Standard. For more information visit the Esri I3S Spec GitHub page.

The Open Geospatial Consortium (OGC) seeks public comment on the formation of the new Sample Markup Language for Artificial Intelligence Machine Learning (SampleML-AI/ML) Standards Working Group (SWG), which will focus on developing the Sample Markup Language for AI/ML candidate standard for consideration by OGC membership for approval as an OGC Standard. Comments are due by May 5, 2021.

Artificial Intelligence (AI) is expected to play a crucial role in many domains and will revolutionize existing technologies. During the last decade, Machine Learning (ML) techniques, especially Deep Learning, have improved significantly due to an abundance of data and advancements in high-performance computing. ML reorients and transforms geographic information systems (GIS) and Remote Sensing (RS). ML-based applications are now being deployed across diverse markets to provide new solutions and increase human efficiency. Increasingly, the science community is also using these techniques to better harness the ever-increasing volume of Earth Observation (EO) data for geospatial analysis in various domains - such as smart cities, environmental management, and disaster management.

A key component of ML techniques and processes is sample data – data with known provenance, consistent metadata, and quality measurements that can be used to consistently tune and train ML applications. A lack of consistent and known sample data is becoming a bottleneck to advanced EO science applications. The lack of sample data is also causing reproducibility issues and making it difficult to compare results across studies.

Therefore, it is expected that the geospatial community should define stricter specifications and policies to enhance discovery and sharing sample data, in particular to develop a standard Sample Markup Language for AI/ML to document, store, and share the geospatial sample data following the FAIR data management principles of Findability, Accessibility, Interoperability, and Reusability.

The SampleML-AI/ML SWG will develop this Sample Markup Language. The geospatial sample data categories will initially include remote sensing imagery, moving features (typically vehicle trajectories), and related spatial content. The SWG will define a model and encodings to exchange and retrieve the sample data across the Web. The SWG will investigate the feasibility and interoperability of OGC standards to use and share geospatial sample data in ML applications and describe gaps and issues that can lead to a new geospatial standard.

Sample data should have sufficient metadata in a machine-readable standard format, include general spatiotemporal information and sample data-specific attributes to facilitate data discovery and query. Where available, the proposed standard will use existing industry standards commonly used by developers.