Remote　sensing　data　have　become　an　important　data　source　for　urban　and　regional　change　detection,　owing　to　their　advantages　of　authenticity,　objectivity,　immediacy,　and　low　cost.　The　method　of　collection　and　management　for　remote　sensing　change　detection　samples　(RS_CDS)　assumes　a　crucial　role　in　the　effectiveness　of　remote　sensing　intelligent　change　detection　(RSICD).　To　achieve　rapid　collection　and　real-time　sharing　of　RS_CDS,　this　study　proposes　a　grid　collection　and　management　model　of　RS_CDS　based　on　GeoSOT　(GCAM-GeoSOT),　including　the　grid　collection　method　of　RS_CDS　(GCM-SD)　and　grid　management　method　of　RS_CDS　(GMM-SD).　To　verify　the　feasibility　and　retrieval　efficiency　of　GMM-SD,　Oracle　and　PostgreSQL　databases　were　combined　and　the　retrieval　efficiency　and　database　capacity　were　compared　with　the　corresponding　spatial　databases,　Oracle　Spatial　and　PostgreSQL　+　PostGIS,　respectively.　The　experimental　results　showed　that　GMM-SD　not　only　ensures　the　reasonable　capacity　consumption　of　the　database　but　also　has　a　higher　retrieval　efficiency　for　the　RS_CDS.　This　results　in　a　noteworthy　comprehensive　performance　enhancement,　with　a　47.63%　improvement　compared　to　Oracle　Spatial　and　a　40.24%　improvement　compared　to　PostgreSQL　+　PostGIS.