Accurate　spatialization　of　socioeconomic　data　is　conducive　to　understand　the　spatial　and　temporal　distribution　of　human　social　development　status　and,　thus,　effectively　support　future　scientific　decision-making.　This　study　focuses　on　population　mapping,　which　is　a　classical　spatialization　of　macroeconomic　data　of　the　social　economy.　Traditional　population　mapping　based　on　rough　grids　or　administrative　divisions　such　as　townships　often　has　deficiencies　in　the　accuracy　of　spatial　pattern　and　prediction.　In　this　article,　hence,　we　employ　residential　geo-objects　as　basic　mapping　units　and　formalize　the　problem　as　a　spatial　prediction　process　using　machine-learning　(ML)　methods　with　high-spatial-resolution　(HSR)　satellite　remote　sensing　images　and　multisource　geospatial　data.　The　indicators　of　population　spatial　density,　including　residential　geo-objects＇　area,　building　existence　index,　terrain　slope,　night　light　intensity,　density　of　point　of　interest　(POI)　and　road　network　from　Internet　electronic　maps,　and　locational　factors　such　as　the　distances　from　road　and　river,　are　jointly　applied　to　establish　the　relationship　between　these　multivariable　factors　and　quantitative　index　of　population　density　using　ML　algorithms　such　as　Random　Forests　and　XGBoost.　The　predicated　values　of　population　density　from　the　mined　nonlinear　regression　relation　are　further　used　to　calculate　the　weights　of　disaggregation　of　each　unit,　and　then　the　population　quantity　distribution　at　the　scale　of　residential　geo-objects　is　obtained　under　the　control　of　the　total　amount　of　population　statistics.　Experiments　with　a　county　area　show　that　the　methodology　has　the　ability　to　achieve　better　results　than　the　traditional　deterministic　methods　by　reproducing　a　more　accurate　and　finer　geographic　population　distribution　pattern.　Meanwhile,　it　is　found　that　the　optimization　of　mapping　results　may　benefit　from　the　multisources　geospatial　data,　and　thus　the　methodological　framework　can　be　recommended　to　be　extended　to　other　spatialization　areas　of　socioeconomic　data.