Geospatial data are a critical part of the information required in the digital transformation of the agricultural sector, but it is yet difficult for farmers to integrate them into their daily work. This is why better tools to facilitate the discovery, creation, quality analysis, and integration of geospatial data across all scales, from Earth observation and remote sensing to in-situ captured data and on-vehicle sensors, are needed to boost the digital transformation of this sector.
During the previous decades, distributed geospatial information infrastructures have been developed in order to provide standardized, interoperable access to geospatial data and services based on a variety of systems, many times developed with different technologies and based on different geospatial data models and georeference systems. In parallel to this, the Digital Earth initiative has provided a vision to guide the integration of geospatial information, services and models at a global scale. This has been materialized into a number of end-user applications, e.g., virtual globes such as the well-known Google Earth, but it is yet far from the integrated platform that was originally envisioned.
All these initiatives have been focused on exploiting existing data, so they have not questioned the dominating models of geospatial information representation. These are the vector model, where data entities have geometries, e.g., points, lines or polygons, defined on given coordinate reference systems, and the raster model where the space is typically divided into rectangular cells and the reality is then sampled on these cell locations and quantized onto them, as for example in a satellite image taken on the visible spectral range where each pixel corresponds to one of these cells.
Nevertheless, there is another model for geospatial information. Discrete Global Grid Systems (DGGS) tessellate the surface of the Earth in discrete cells. These cells form hierarchies of nested, multi-resolution grids, and thus offer a powerful tool to facilitate the integration of data measured at different scales. DGGSs provide uniform sampling and quantization strategies for the data, and a way to uniquely identify each cell.
DGGSs offer a number of potential advantages over the current models, although it is an active research topic finding out the best use cases for them. The DGGS model was proposed decades ago, but it is now that the standardization initiative of the Open Geospatial Consortium (OGC) is beginning to make it possible to consider them for the case of geospatial information infrastructures, where interoperability is a crucial requirement.
This project proposes to advance the knowledge and to create the technology required to set up a standards-based geospatial information infrastructure for data modelled on a DGGS, to develop the necessary data discovery, retrieval, quality assurance and transformation pipelines, to create an in-situ data capture application to test the integrability of data created at different scales, and to use this information infrastructure to support a geospatial information system for the management of the sustainable use of pesticides in a farm, as a relevant example of digitalization in the agricultural sector.