## 1 Introduction  
Earth Observation (EO) data services are becoming very popular because of the significant increase in satellite missions and geospatial cloud-based platforms such as the Google Earth Engine and Microsoft Planetary [1, 2]. New investments in the space economy have boosted the technological transfer in different fields opening new opportunities in terms of applied science [3, 4, 5]. It is worth noting that both the public and private sectors in alpine and rural areas are still far behind. Therefore, it is more crucial to fill this gap by realizing and exporting useful EO tools to strengthen the monitoring and study of the biophysical components of different territories and use public funds more efficiently and effectively [6, 7, 8]. This would permit mountainous areas to keep up, stay competitive and bring innovation even in apparently distant contexts. In particular, the Copernicus program, as well as many other scientific EO programs around the world, provide vast amounts of geographical datasets that may aid in achieving European and international technological transfer goals of to face considerable issues such as climate change, sustainable development and social inclusion worldwide [9, 10, 11]. Recently, Google announced its realization of the Dynamic World project. Dynamic World is a near real-time 10 m spatial resolution global land use land cover (LULC) dataset produced using deep learning and is freely available and openly licensed. It is the result of a partnership between Google and the World Resources Institute to produce a dynamic dataset of the physical material on the surface of the Earth. Dynamic World is intended to be used as a data product for users to add custom rules and assign final class values, producing derivative land cover maps. The main key innovation of Dynamic World is represented by near-real time image enabling the mapping of LULC every 5 days depending on the location and adopting Sentinel-2 top-of-atmosphere per-pixel probabilities across nine land cover classes with a 10 m GSD. This EO service based on the Google Earth Engine is very powerful but regarding the accuracy in mountainous areas such as the Alps, it presents considerable criticalities. First, there is a strong confusion between the concept of land coverage and use (the first can be mapped by satellite, the second can only generally be used for certain uses such as moving). Second, the system uses a probabilistic approach, not a deterministic one. Therefore, at a planning level, some problems can be encountered (e.g., a misleading biophysical component defined as an incorrect class for a forest or built up areas defined as water after snowmelt in alpine areas). Third, the Dynamic World training set distribution is almost completely absent in mountainous and alpine areas. Typically, these areas are the most complex to map for remote sensing. Furthermore, the classes are designed to map global changes at high resolution using fewer classes which do not answer to the local needs for land covers that adopt the new European EAGLE guidelines and that have local robust accuracies that are only obtainable with a continuous ground truth data validation set. The EIONET Action Group on land monitoring in Europe (known as EAGLE group) is an open assembly of technical experts from different European Economic Area (EEA) Member States, mostly in their roles as national reference center (NRC) on LC. Currently, the development of the EAGLE concept and methodology is being funded by the EEA within the framework of the Copernicus program. In Italy, LC monitoring is performed by the Istituto Superiore per la Protezione e la Ricerca Ambientale (ISPRRA). For their activities and policies, many user communities such as decision-makers, non-governmental organizations, European communities, scientists and researchers require various sorts of LC information [12].