The　Copernicus　Sentinel-2　mission　operated　by　the　European　Space　Agency　(ESA)　provides　comprehensive　and　continuous　multi-spectral　observations　of　all　Earth＇s　land　surface　since　mid-2015.　Clouds　and　cloud　shadows　significantly　decrease　the　usability　of　optical　satellite　data　especially　in　agricultural　applications;　therefore,　an　accurate　and　reliable　cloud　mask　is　mandatory　for　effective　Earth　Observation　(EO)　optical　data　exploitation.　During　the　last　years,　image　segmentation　techniques　have　developed　rapidly　with　the　exploitation　of　neural　networks　capabilities.　In　this　perspective,　the　KappaMask　processor　using　U-Net　architecture　was　developed　with　the　ability　to　generate　a　classification　mask　over　northern　latitudes　into　the　following　classes:　clear,　cloud　shadow,　semi-transparent　cloud　(thin　clouds),　and　cloud,　invalid.　For　training,　a　Sentinel-2　dataset　covering　Northern　European　terrestrial　area　was　labeled.　KappaMask　provides　a　10　m　classification　mask　for　Sentinel-2　Level-　2A　(L2A)　and　Level-1C　(L1C)　products.　The　total　dice　coefficient　on　the　test　dataset,　which　was　not　seen　by　the　model　at　any　stage,　was　80%　for　KappaMask　L2A　and　76%　for　KappaMask　L1C　for　27　clear,　cloud　shadow,　semi-transparent,　cloud　classes.　Comparison　with　rule-based　cloud　mask　28　methods　was　then　performed　on　the　same　test　dataset,　where　Sen2Cor　reached　59%　dice　coefficient　29　for　clear,　cloud　shadow,　semi-transparent,　cloud　classes,　Fmask　61%　for　clear,　cloud　shadow,　cloud　30　classes,　and　Maja　51%　for　clear　and　cloud　classes.　The　closest　machine　learning　open-source　cloud　classification　mask,　S2cloudless,　has　63%　dice　coefficient　providing　only　cloud　and　clear　classes,　while　KappaMask　L2A　with　a　more　complex　classification　schema　outperformed　S2cloudless　by　17%.