Enriching Earth observation datasets through semantics for climate change applications: The EIFFEL ontology

Abstract

Background: Earth Observation (EO) datasets have become vital for decision support applications, particularly from open satellite portals that provide extensive historical datasets. These datasets can be integrated with in-situ data to power artificial intelligence mechanisms for accurate forecasting and trend analysis. However, researchers and data scientists face challenges in finding appropriate EO datasets due to inconsistent metadata structures and varied keyword descriptions. This misalignment hinders the discoverability and usability of EO data. Methods: To address this challenge, the EIFFEL ontology (EIFF-O) is proposed. EIFF-O introduces taxonomies and ontologies to provide (i) global classification of EO data and (ii) linkage between different datasets through common concepts. The taxonomies specified by the European Association of Remote Sensing Companies (EARSC) have been formalized and implemented in EIFF-O. Additionally, EIFF-O incorporates: 1. An Essential Climate Variable (ECV) ontology, defined by the Global Climate Observing System (GCOS), is embedded and tailored for Climate Change (CC) applications. 2. The Sustainable Development Goals (SDG) ontology is included to facilitate linking datasets to specific targets. 3. The ontology extends schema.org vocabularies and promotes the use of JavaScript Object Notation for Linked Data (JSON-LD) formats for semantic web integration. Results: EIFF-O provides a unified framework that enhances the discoverability, usability, and application of EO datasets. The implementation of EIFF-O allows data providers and users to bridge the gap between varied metadata descriptions and structured classification, thereby facilitating better linkage and integration of EO datasets. Conclusions: The EIFFEL ontology represents a significant advancement in the organization and application of EO datasets. By embedding ECV and SDG ontologies and leveraging semantic web technologies, EIFF-O not only streamlines the data discovery process but also supports diverse applications, particularly in Climate Change monitoring and Sustainable Development Goals achievement. The open-source nature of the ontology and its associated tools promotes rapid adoption among developers. Plain language summary: Satellites and other tools used to observe Earth provide a lot of data that can help us make decisions, like predicting the weather or understanding climate change. However, these large collections of data are often disorganized and described differently by different people, which makes it hard for scientists and researchers to find and use the information they need. To solve this problem, the paper introduces the EIFFEL Ontology (EIFF-O). This is a new system designed to organize and link Earth observation data in a way that makes it easier to find and use. It does this by creating common categories and connections between different data sets. Three important features of EIFF-O are: It focuses on climate change, helping to categorize important climate data. It connects the data to global goals for sustainable development, which helps in achieving specific environmental targets.

It links these data sets to the wider internet in a way that makes them easier to share and understand.

The EIFF-O system is freely available for anyone to use and comes with tools that help developers quickly implement it in their projects. This makes it easier for everyone to access and benefit from Earth observation data.