This paper presents a case study of automatic classification of the remotely sensed Sentinel-2 imagery, from the EU Copernicus program. The work involved a study site, located in the area next to the city of Pavia, Italy, including fields cultivated by three farms. The aim of this work was to evaluate the so-called supervised classification applied to satellite images and performed with Esri's ArcGIS Pro software and Machine Learning techniques. The classification performed produces a land use map that is able to discriminate between different land cover types. By applying the Support Vector Machine (SVM) algorithm, it was found that, in our case, the pixel-based method offers a better overall performance than the object-based, unless a specific class is exclusively taken into consideration. This activity represents the first step of a project that fits into the context of Precision Agriculture, a recent and rapidly developing research area, whose aim is to optimize traditional cultivation methods.
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