Objective

WP3 provides the technological backbone of the project by tailoring the
technological background to project needs. The development of proper codes aimed at estimating risks in vine cultivation in different environmental contexts automatically is still challenging. This is mainly due to the dependence of this phenomenon on Earth’s surface characteristics, the huge amount of needed data to assess it, and, lastly, technological limitations. In recent years, new generation satellite missions equipped with innovative sensors from spectral and spatial resolution points of view have been
introduced. Developing novel satellite data procedure by using most of them, such as PlanetScope and Landsat 9 (launched in
September 2021), have not been explored to estimate the above-mentioned variables yet. Therefore, after investigating the most
suitable features to detect the risks in vine culture and analyzing the most suitable satellite imagery characteristics to extract such
information, conventional models as well as newest ones will be implemented to test their performance and to point out their
limitations. Thus, a proper rule-based model, based on the application of Machine Learning (ML) algorithm, will be also developed and tested

Methodology

WP3 integrates and adapt all required and existing (background) tools in order to develop a proper code to handle
geospatial big data in a free and open-Source cloud environment. Contrary to desktop software, its usage brings out more benefits. Firstly, involving many processors in running the scripts, the process is speeded up significantly, and the problems linked to the storage, the processing, and the analysis of a large volume of geospatial data are cleaned out. This results in the possibility to extract the outputs in a short time and increment the number of tests to perform. Then, cloud platforms are more flexible, and they allow the implementation of own codes, adapted to meet users’ needs. Nevertheless, such frameworks are still in progress and, consequently, the most of existing algorithms have not been implemented yet. Lastly, the application of Free and Open-Source Software for Geographic information systems (FOSS4G) is perfectly in line with Open Geospatial Consortium (OGC) and EU directive INSPIRE.

Tasks

Task 3.1. EO and Geospatial data inventory and ingestion aimed at collecting and monitoring all EO data.

Task 3.2. EO data management through the development of an adequate code.