The finer-scale spatial distribution of population within cities and towns is of great significance for studying the human-resource-environment interrelationships and supporting smart city construction and resource allocation. It also helps the government to assist disaster assessments and land use planning, manage the distribution of population and resource, and promote urban sustainable development. However, existing population spatialization methods are insufficient to spatialize population in cities and towns at fine scales. With the rapid development of geospatial big data and the popularity of high-resolution remote sensing data, this study proposes a method to estimate urban population distribution at fine scales through fusing multi-source spatial data. First, a total of 12 variables having large correlations （R2>0.7） with the population were selected to estimate the population distribution in Ningbo city, including the build-up area, distance to the road, nighttime lights, business service center, EAHSI index, kindergarten, park, primary school, gas station, hospital, and bus station and coach station. First, the population distribution areas are determined by urban functional zones, then a random forest model was used to train a population estimation model with the selected 12 variables; finally, the 2018 population data of the Ningbo were redistributed into 500 m grids by the trained estimation model. The importance of the chosen variables were analyzed using the random forest model. The results demonstrate that the presented population estimation model reaches an accuracy of 81.2% at sub-district scale with the MRE of 0.29 and the RMSE of 3279.89. Therefore, the population estimation model presented in this study can accurately predict the population distribution at the sub-district. This study also conducted the accuracy verification at the grid scale with the MRE of 17.16 and the RMSE of 1149.9. According to the importance of variables computed by the random forest model, it is found that the importance of the variable building area is about 0.22, which has the largest influence on the population distribution, followed by the variables, distance to road, nighttime lights, business service center, EAHSI ( Elevation-Adjusted Human Settlement Index), kindergarten, and park. The accuracy verification at the grid level is of great significance for studying the fine population distribution in cities. However, the estimation accuracy is still not very high in some cases where the populations of some grids are either overestimated or underestimated. The lack of building height information is a possible reason. In addition, deep learning methods will be explored to improve accuracy in future.