Sand　dune　patterns　(SDPs)　are　spatial　aggregations　of　dunes　and　interdunes,　exhibiting　distinct　morphologies　and　spatial　structures.　Recognizing　global　SDPs　is　crucial　for　understanding　the　development　processes,　contributing　factors,　and　self-organization　characteristics　of　aeolian　systems.　However,　the　diversity,　complexity,　and　multiscale　nature　of　global　SDPs　poses　significant　technical　challenges　in　the　classification　scheme,　sample　collection,　feature　representation,　and　classification　method.　This　study　addresses　these　challenges　by　developing　a　novel　global　SDP　classification　approach　based　on　an　advanced　deep-learning　network.　Firstly,　we　established　a　globally　applicable　SDP　classification　scheme　that　accommodates　the　diversity　nature　of　SDPs.　Secondly,　we　developed　an　SDP　semantic　segmentation　sample　dataset,　which　encompassed　a　wide　array　of　SDP　representations.　Thirdly,　we　deployed　the　SegFormer　network　to　automatically　capture　detailed　dune　structures　and　developed　a　weighted　voting　strategy　to　ensure　scale　adaptability.　Experiments　utilizing　Landsat-8　imagery　yielded　a　commendable　overall　accuracy　(OA)　of　85.43　%.　Notably,　most　SDP　types　exhibited　high　classification　accuracies,　such　as　star　dunes　(97.43　%)　and　simple　linear　dunes　(87.17　%).　The　weighted　voting　strategy　prioritized　the　predictions　of　each　type,　resulting　in　a　1.41　%similar　to　7.91　%　improvement　in　OA　compared　to　the　single-scale　classification　and　average　voting　methods.　This　innovative　approach　facilitated　the　generation　of　a　high-quality,　fine-grained,　and　global-scale　SDP　map　at　30　m　resolution　(GSDP30),　which　not　only　directly　provides　the　spatial　distribution　of　global　SDPs　but　also　serves　as　valuable　support　for　understanding　aeolian　processes.　This　study　represents　the　first　instance　of　producing　such　a　comprehensive　and　globally　applicable　SDP　map　at　this　fine　resolution.