Semantic Integration of Raster Data for Earth Observation on Territorial Units

Raster is a common data format in satellite image processing. A raster allows us to model geographic phenomena as a regular surface in which each cell (or pixel) is associated with a phenomenon value. Many rasters can be provided for the same geographic area, for the same phenomenon at different dates or different phenomena; they can be compared, combined, used to generate a new one, etc. A recurrent issue however is to transfer data from pixels to features to qualify territorial units, which requires complex aggregation processes. This paper addresses this issue thanks to a semantic data integration process based on spatial and temporal properties. We propose i) a modular and generic ontology used for the homogeneous representation of data qualifying a geographical area of interest; and ii) a Semantic Extraction, Transformation, and Load (ETL) process that relies on the ontology and data extracted from rasters and that maps the aggregated data to the corresponding areas. We evaluate our approach in terms of the (i) adaptability of the proposed model and pipeline to accommodate different use cases, (ii) added value of the generated datasets in helping decision making, and (iii) approach scalability.