In　recent　years,　crowd　analysis　has　been　widely　studied　due　to　its　realistic　applications　in　many　areas.　In　this　paper,　we　pose　a　novel　challenge　for　monitoring　large-scale　crowd　scenes　from　an　aerial　view　via　estimating　specific　crowd　flow　for　each　partitioned　region.　However,　existing　methods　are　difficult　to　estimate　the　specific　crowd　flow　for　each　region　flexibly,　simply,　and　accurately,　especially　lacking　clear　crowd　appearance　features　from　a　top-down　view.　To　accomplish　this,　we　present　a　crowd　flow　estimation　model　whose　goal　is　to　estimate　the　flow　into　and　out-of　a　certain　region　over　any　given　time　span.　Specifically,　we　set　up　a　two-stream　network　that　jointly　regresses　crowd　density　and　individual　velocities,　so　as　to　directly　approximate　the　instantaneous　flow　at　each　location.　To　enhance　the　flow　estimation,　we　utilize　the　local　relationships　between　crowd　distribution　and　individual　velocities　via　a　proposed　locality-confined　attention　module.　Furthermore,　we　incorporate　the　additional　spatio-temporal　regularization　for　the　top-down　view　by　reversing　future　frames　via　the　proposed　inverse-temporal　loss.　In　experiments,　we　apply　drone-based　overhead　crowd　videos　to　evaluate　our　approach　in　the　task　of　crowd　flow　estimation,　and　we　show　that　our　approach　surpasses　the　performance　of　prior　methods　and　can　also　be　applied　in　a　variety　of　crowd　analysis　applications　for　understanding　social　scenes.　©　2025　Elsevier　Ltd