Blog / September 10, 2021
With the 2016 release of the FAIR Principles (and longer running GEO DSP and DMP), the scientific community has been increasingly moving toward open science that is findable, accessible, reusable, and interoperable for both people and machines. At the same time, there is growing recognition that the current state of practice – public data and code repositories, as well as implementations of the GEOSS data sharing and data management principles – falls short of this ideal. While the GEO Knowledge Hub, UN Global Platform and others provide places to share data, algorithms, and models online, none of this content can communicate seamlessly with the rest, which is emblematic of the wider challenges of achieving interoperability and reusability.
The Artificial Intelligence for Environment and Sustainability (ARIES) Project has recognized these challenges and has been working since 2007 to achieve semantic interoperability - a system in which computers can understand the meaning of the data being exchanged and reuse it accordingly. In practice, this application of the semantic web enables ARIES’ users to simply select a scientific modeling problem by specifying a region, a spatial and temporal scale, and a phenomenon of interest through a web interface. Doing so triggers a computer search of the web for networked models and data for each query, selects those that are most appropriate for the context of interest, assembles and runs the models, and returns the results and provenance that transparently shows all data and modeling choices made along the computational chain. By contributing their own data and models on an expandable network, scientists and data providers can contribute new data and models across disciplines, increasing the power and flexibility of the ARIES system for users while making semantic interoperability a reality. The ARIES infrastructure includes (1) multidisciplinary semantics to logically and consistently label all data and model elements, (2) open, machine-accessible data and models, and (3) open-source software to enable machines to quickly integrate new scientific knowledge.
In April 2021, ARIES and the UN Statistics Division released ARIES for SEEA, a platform to support widespread adoption of the System for Environmental Economic Accounting-Ecosystem Accounting (SEEA EA). SEEA EA measures nature’s contributions to national and global economies, providing a critical way to monitor sustainability. However, SEEA EA requires significant use of remote sensing, GIS, and biophysical modeling – skills beyond reach for national statistical offices in many nations. With ARIES for SEEA, users can access and run the data and models they need through a simple interface to produce accounts and get back spatial data, tables, and text to interpret the results. ARIES for SEEA can provide results anywhere on Earth using global data and models, but at the same time can be quickly customized using local data, models, and parameterizations - after which such customization becomes available to future users.
According to Daniel Juhn, steering committee chair of the EO4EA GEO Initiative, “Ecosystem accounting will revolutionize our ability to incorporate nature’s values into our decisions; given our global challenges, it’s urgent to support account development at scale. By making needed data and models interoperable, ARIES for SEEA harnesses the GEO community’s collective energies towards translating shared data into information that countries can use. With greater community buy-in and use, ARIES can offer powerful solutions for better monitoring across a range of global goals like the Sustainable Development Goals, Paris Agreement, Sendai Agreement and the upcoming Global Biodiversity Framework.”
Next steps for the wider success of this interoperability-driven initiative involve greater community use of and contributions to its distributed platform. In this way, we can envision a future in which SEEA EA, ecosystem service assessments, and related indicators can be (1) rapidly compiled as new science emerges, (2) quickly produced to show trends as new Earth observation data become available, enabling (3) robust international comparisons using common global data and simultaneous country-specific customization. This vision moves high-quality, meaningful information from scientists into the hands of decision makers, the public, and the media as quickly as possible.
With a viable path toward implementing and scaling up the semantic web vision, we are at a critical stage to significantly improve Earth observation data interoperability and take the science that it enables to the next level. We have developed an interoperability strategy that describes the technical details of how to use and contribute to the ARIES Network , which complements a full technical description of the ARIES approach. We are seeking partner organizations, including data providers, ready to participate as nodes in a semantic knowledge network aiming to use interoperable and reusable science to inform critical national and global sustainability initiatives.
Please contact us (email@example.com) to learn more and to join us in this journey!
This blogpost was written by:
The ARIES Team hosted at the Basque Centre for Climate Change - BC3 (Ferdinando Villa, Stefano Balbi, Alessio Bulckaen), U.N. Statistics Division (Alessandra Alfieri, Bram Edens), U.N. Environment Programme (William Speller), GEO (Steven Ramage), edited by Ken Bagstad, U.S. Geological Survey
Advances in Satellite-Based Land Cover Mapping and Monitoring - Special issue for the journal Remote Sensing
Outcomes of the Forum on Innovation in Remote Sensing Technologies for Accelerated Climate Action, leading to COP26
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