The　differences　in　the　definition　of　urban　areas　lead　to　our　contrasting　or　inconsistent　understanding　of　global　urban　development　and　their　corresponding　socioeconomic　and　environmental　impacts.　The　existing　urban　areas　were　widely　identified　by　the　boundaries　of　built-environment　or　social-connections,　rather　than　urban　entities　that　are　essentially　the　spatial　extents　of　human　activity　agglomerations.　Thus,　this　study　has　attempted　to　map　and　evaluate　global　urban　entities　(2000-2020)　from　a　perspective　of　an　updated　urban　concept　of　urban　entities　based　on　the　consistent　remotely　sensed　nighttime　light　data.　First,　a　K-means　algorithm　was　developed　to　cluster　urban　and　non-urban　pixels　automatically　in　consideration　of　global　region　division.　Then,　a　post-processing　was　conducted　to　enhance　the　temporal　and　logical　consistency　of　urban　entities　during　the　study　period.　Rationality　assessment　indicates　that　urban　entities　derived　from　remotely　sensed　nighttime　light　data　more　effectively　reflect　the　spatial　agglomeration　extents　of　human　activities　than　those　of　physical　urban　areas.　Global　urban　entities　increased　from　157,733　km(2)　in　2000　to　470,632　km(2)　in　2020　accompanied　by　a　differentiated　urban　expansion　at　global,　continental,　and　national　levels.　Our　study　provides　long-time　series　and　fine-resolution　datasets　(500　m)　and　new　research　avenues　for　spatiotemporal　analysis　of　global　urban　entity　expansion　with　the　improvement　of　the　understanding　of　urbanization　and　the　emergence　of　effective　urban　mapping　theories　and　approaches.