The　production,　dissipation,　and　variation　of　cloud　play　important　roles　in　the　weather　forecast　such　as　rain,　snow　and　hail,　which　are　typically　reflected　in　changes　in　cloud　types　and　cloud　cover.　It　is　critical　to　obtain　the　cloud　cover　of　each　type　of　cloud　quickly　and　accurately　in　the　constantly　changing　ground-based　cloud　images　in　order　to　capture　and　predict　weather　changes,　and　the　prerequisite　is　to　implement　automatic　ground-based　cloud　image　segmentation　technology　classified　by　cloud　type.　To　implement　this　technique,　this　paper　introduces　a　full　convolutional　network　structure　in　deep　learning　to　implement　a　segmentation　algorithm　for　ground-　based　cloud　images　at　the　semantic　level.　We　first　create　the　ground-based　cloud　image　database　for　semantic　segmentation　(GBCSS).　This　database　includes　ten　categories　of　cloud　and　one　category　of　background　for　a　total　of　eleven　categories,　with　pixel-level　labels　for　each　image.　Then,　using　convolutional　neural　network　structures,　we　design　five　full　convolutional　networks　and　evaluate　the　semantic　segmentation　effects　on　GBCSS.　The　experimental　results　show　that　all　five　full　convolutional　networks　have　good　segmentation　accuracy　of　on　GBCSS,　with　the　highest　accuracy　reaching　75.7%,　laying　the　foundation　for　the　application　of　semantic　segmentation　in　ground-based　cloud　image.　©　2022　IEEE.