Matthias Mohr

STAC Browser is a full-fledged web interface for browsing and searching static STAC catalogs and STAC APIs. It has been rewritten from scratch with a lot of new functionality. This talk will introduce STAC Browser, showcase new functionality and uncover some unexpected gems such as the broad range of customization possibilities. Lastly, the presentation will guide you through a set of best practices for your static STAC catalog or STAC API so that you get the most out of STAC Browser with regards to functionality and user experience.

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The SpatioTemporal Asset Catalog (STAC) (and Cloud Native Geospatial ecosystem) for/in JavaScript has evolved in the last year. This talk will update you on the current state of the ecosystem and gives an outlook on what is missing. For STAC talk will cover libraries such as stac-js, stac-layer, stac-browser, stac-node-validator, and more. We'll dive into what the libraries do, how they relate to each other and give you some hints how you get started. At the end, a short excursion into the cloud-native geospatial ecosystem in JavaScript for COG, geoparquet, geozarr and other file formats will be provided as well.

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The SpatioTemporal Asset Catalog (STAC) specifications are a flexible language for describing geospatial information across domains and for a variety of use cases. This talk will present the current state of the specifications, which includes the core STAC specification and the API specification built on top of OGC APIs. While the core specification has been stable for roughly two years and doesn't need a lot of updates, the API specification got numerous updates and is finally close to a stable release. This presentation digs into additions to STAC extensions and the latest community developments. We survey the updates to the open-source STAC ecosystem, which includes software written in Python, Node.js, and more. Finally, let's also look into the near future.

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Other people: Matthew Hanson

openEO is a new API specification for Earth Observation data cubes that supports data extraction, processing and viewing.

Both the standard and its implementations are Open Source projects, which itself rely on Open Source libraries under the hood, such as GRASS GIS, GDAL, Geotrellis, Rasdaman, or provide a standardized interface to proprietary systems such as Google Earth Engine. Client implementations are available for JavaScript, R, Python, QGIS and web browsers.

This talk will show an overview of the main capabilities, and available client and backend implementations.

Earth Observation data are becoming too large to be downloaded locally for analysis. Also, the way they are organised (as tiles, or granules: files containing the imagery for a small part of the Earth and a single observation date) makes it unnecessary complicated to analyse them. The solution to this is to store these data in the cloud, on compute back-ends, process them there, and browse the results or download resulting figures or numbers. Unfortunately, data and APIs are too often proprietary solutions and lock-in to a service provider happens easily so an interoperable standard across service providers is much needed.

The aim of openEO is to develop an open API to connect R, Python, JavaScript and other clients to big Earth observation cloud back-ends in a simple and unified way. With such an API, each client can work with every service provider, and it becomes possible to compare them in terms of capacity, cost, and results (validation, reproducibility).

The specification is centered around Earth Observation data cubes that supports data extraction, processing and viewing. It specifies a set of common processes to be used so that switching between service providers is less of a problem. Both the standard and its implementations are Open Source projects. Under the hood, the client and backend implementations rely on Open Source libraries, such as GRASS GIS, GDAL, Geotrellis, Rasdaman, but also provide a standardized interface to proprietary systems such as Google Earth Engine.

The talk introduces STAC, the SpatioTemporal Asset Catalog specification. It aims to enable a cloud-native geospatial future by providing a common layer of metadata for better search and discovery. It is an emerging open standard to catalog and expose geospatial data from different sources either in a static or dynamic way.

We’ll cover the core set of metadata fields for STAC Catalogs, Collections, and Items first, along with available extensions for describing different types of data (EO, SAR, Point Cloud, etc.). With the basics of STAC in hand, the talk will go through the Open Source ecosystem for working with STAC metadata: validators, graphical user interfaces and client command line tools and libraries for search, access, and exploitation.

The SpatioTemporal Asset Catalog (STAC) specification is an emerging standard to catalog and expose geospatial data from different sources. It aims to enable a cloud-native geospatial future by providing a common layer of metadata for better search and discovery.

This talk gives a detailed overview of STAC and the way it allows for static and dynamic implementations at the same time. The simple concept of static catalogs living alongside the data on cloud file storage (e.g., AWS S3, GCS) by adding small JSON files is highlighted before talking through the dynamic searchable APIs built on top of the new OGC API – Features standard.

The talk will cover the core set of metadata fields for STAC Catalogs, Collections, and Items, along with available extensions for describing different types of data (EO, SAR, Point Cloud, etc.). With the basics of STAC in hand, the talk will go through the Open Source ecosystem for working with STAC metadata: validators, graphical user interfaces and client command line tools and libraries for search, access, exploitation and API generation.

The specification is an open standard developed on GitHub by a wide range of organizations with a strong focus on extensibility to support various domains. It encourages interested parties to extend the specification for their needs for a future of interoperable discovery and work with geospatial data. An ecosystem of Open Source tooling is evolving around the specification.