Readily available and accurate maps of population distribution are of critical importance in decision-making. In this study， a new methodology based on ensemble learning technology is introduced that leverages geospatial big data and multi-source remote sensing data for high-resolution and high precision population mapping. Population predictor variables were extracted from Tencent location big data， points of interest and remote sensing data. Using three individual machine learning algorithms （i.e. XGBoost， neural network， and random forest） and the Stacking ensemble learning method， four population prediction models were established to disaggregate the 2020 census population data of Zhejiang Province to grids with 100 m resolution. The results show that： （1） Among three machine learning algorithms， random forest has the best prediction performance. Compared to individual machine learning algorithms， the Stacking ensemble learning strategy has good generalization performance， alleviates the high-value overflow issue， and reduces prediction errors； （2） The results from the ensemble show that the high population density in Zhejiang Province located in the city's core region， with a peak value of 500 people/grid. Population density decreases in steps with increasing distance from urban centers； （3） The gridded population data from the stacking ensemble outperform the WorldPop dataset in terms of higher population density in urban centers and data integrity. This study provides new methods and technical means for rapidly and accurately population mapping in the era of big data.