Abstract
Semantic technologies are at the core of Earth Observation (EO) data integration, by providing an infrastructure based on RDF representation and ontologies. Because many EO data come in raster files, this paper addresses the integration of data calculated from rasters as a way of qualifying geographic units through their spatio-temporal features. We propose (i) a modular ontology that contributes to the semantic and homogeneous description of spatio-temporal data to qualify predefined areas; (ii) a Semantic Extraction, Transformation, and Load (ETL) process, allowing us to extract data from rasters and to link them to the corresponding spatio-temporal units and features; and (iii) a resulting dataset that is published as an RDF triplestore, exposed through a SPARQL endpoint, and exploited by a semantic interface. We illustrate the integration process with raster files providing the land cover of a specific French winery geographic area, its administrative units, and their land registers over different periods. The results have been evaluated with regards to three use-cases exploiting these EO data: integration of time series observations; EO process guidance; and data cross-comparison.
Highlights
Earth Observation (EO) is a domain that has greatly evolved in the previous years thanks to large-scale Earth monitoring programs, such as the US Landsat Program and the EU Copernicus Program
With the Copernicus program launched by the European Space Agency (ESA), data are collected by satellites and combined with observation data from sensor networks on the Earth’s surface
The major advantage of this approach is to facilitate further processing, analysis, or reasoning on the materialized RDF data. This choice is motivated by the following reasons: (i) it is not easy to run an on-demand mapping since the data sources we considered are available in different formats (JSON/GeoJSON, GeoTIFF image, shapefile or even remote compressed files), which requires a pre-processing step of conversion; (ii) Geospatial triplestores can be considered as a warehouse to store semantic data so that data enrichment and linking could be performed; and (iii) different datasets may be offered by different endpoints requiring federation mechanisms, there is currently no query engine mature enough for answering GeoSPARQL queries over such a federation [11]
Summary
Earth Observation (EO) is a domain that has greatly evolved in the previous years thanks to large-scale Earth monitoring programs, such as the US Landsat Program (https://www.usgs.gov/landresources/nli/landsat) and the EU Copernicus Program (http://www.copernicus.eu/en). As a first contribution, we propose a generic vocabulary that allows the semantic and homogeneous description of spatio-temporal data to qualify predefined areas together with their provenance This model is extendable to deal with any kind of observed EO property. A third contribution is the dataset resulting from the integration process of three different sources that we used for experimental validation: land cover data of a specific French winery geographic area, its administrative units, and their land registers. Given a period and a village name or a geographic area defined by its geometry, one can retrieve the land registers in this area and the evolution of their land cover during this period These data serve as the basis for three scenarios: integration of time series observations; EO process guidance; and data cross-comparison.
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