Abstract. The need for effective crop monitoring in large geographical scales has become increasingly important in recent years and constitutes a technological and scientific challenge for remote sensing applications. In Europe, member states of the European Union collect geospatial data in the framework of the Land Parcel Information System (LPIS) for agricultural management and subsidizing farmers. These data can be exploited as training datasets of machine learning classifiers for crop-type mapping applications. However, the way the LPIS data are being generated, concerning primarily errors in the farmers’ declarations in terms of crop-type labels, exact geometries, etc, constrains their direct use in such classification frameworks. In this study, we present and assess a methodology for filtering LPIS data based on geometric and spectral criteria in order to build a trustworthy training dataset for machine learning crop-type classifiers. A new nomenclature was developed, oriented towards the spectral discrimination of crop-type classes and sub-classes in Greece. The filtering methodology was applied at national scale for the agricultural year of 2019 and resulted in the selection of a sub-set of the LPIS parcels that were assessed as the most suitable and reliable to represent the different levels of the nomenclature. The developed filtering procedure was validated against actual crop-type labels derived from field visits, while the resulted filtered data were successfully utilized on various crop-type mapping experiments in Greece.

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<p>The reconstruction of present-day stress and palaeostress trajectories is of paramount importance to study the tectonic regime and its evolution, in a specific area. Its comprehension is crucial also for seismic and volcanic hazard assessment, especially focusing on the shallow crust.</p><p>In the framework of the NEANIAS project (https://www.neanias.eu/), EU H2020 RIA, it has been developed the so called ATMO-Stress service (https://docs.neanias.eu/projects/a2-1-service/en/latest/), an open-source cloud service, currently hosted on the GARR Kubernetes platform, which allows to calculate stress trajectories in plain view, based on the concepts from Lee and Angelier (1994). It is designed to run on modern computers for both academics and non-academics purposes, spanning from research activity to oil and gas industries, natural hazard prevention and management.</p><p>The service is freely accessible at https://atmo-stress.neanias.eu/ and is designed to calculate the stress trajectories for a specific area, considering as input the same type of stress (e.g. σ<sub>Hmax</sub> or σ<sub>Hmin</sub>). Data input can be from different sources (e.g. field data, focal mechanism solutions, in-situ geotechnical measures). They must be listed in a homogeneous ASCII text file or Excel file format, including the geographic coordinates, azimuth of the stress and the angular error. The service is capable of processing data from local to regional scale. Following the principles from Lee and Angelier (1994), the trajectory calculation can be done using different parameters and settings. The outputs can be seen directly on the website and can be downloaded with file formats ready to be imported and analyzed in GIS environment and Google Earth.</p>