Genetic　landform　classification　and　mapping　provide　crucial　information　concerning　the　formation　and　evolution　of　the　earth　surface,　aiding　in　understanding　geological　processes,　climate　variation,　and　driving　mechanisms　of　earth　dynamics.　Conventional　pix　and　object　unit-based　research　paradigms　poss　challenges　in　classifying　and　mapping　the　genetic　landform　on　a　large　spatial　scale,　while　the　terrain　unit-based　method　offers　an　inspiring　opportunity　for　this　typical　landform　task.　In　this　context,　we　herein　developed　a　slope　unit　(SU)-based　landform　classification　framework,　involving　improving　the　mean　curvature　method　for　SU　segmentation,　developing　the　spectra　method　to　determine　the　minimal　area　size　of　SU,　introducing　a　specific　quantitative　indicator　system　for　describing　SU,　and　a　universal　classification　model　based　on　XGBoost.　Using　the　entire　Tibetan　Plateau　as　the　study　area,　we　first　implemented　the　genetic　landform　map　and　divided　it　into　nine　genetic　landform　types　including　aeolian,　arid,　loess,　karst,　periglacial,　fluvial,　glacial,　volcanic-lava,　and　lacustrine.　Classification　results　achieved　an　encouraging　performance　with　overall　accuracy,　kappa　coefficient,　and　mean　accuracy　of　up　to　90.2%,　84.71%,　and　88.65%,　respectively.　Experimental　results　suggested　that　delineated　genetic　landform　regions　and　their　boundaries　are　generally　closely　correlated　with　regional　climatic　conditions,　geological　factors,　or　surface　processes.　Compared　to　pix　or　object-unit-based　methods,　the　SU　delineation　results　possess　internal　homogeneity　and　clear　terrain　boundaries　with　geo-meaning.　Compared　to　the　watershed-unit-based　method,　our　method　is　better　suited　for　large-scale　landform　classification.　In　the　end,　we　confirmed　that　the　proposed　framework　can　be　extended　to　large-scale　planetary　genetic　landform　mapping　via　two　case　studies　on　Mars　and　Moon,　which　is　transferable　yet　straightforward.　In　brief,　we　argued　that　the　proposed　framework　offered　a　powerful　and　effective　method　in　large-scale　genetic　landform　classification,　somewhat　effectively　circumventing　the　limitations　of　existing　landform　classification　methods.