## 1 Introduction
Ensemble learning combines multiple learners into a unified entity through certain strategies to jointly complete tasks and enhance decision accuracy through collective decision-making, primarily involving boosting and bagging algorithms. The Boosting algorithm follows the principle of gradient boosting [23], and updates the model by feeding back the information of each round of model training to the next round, obtaining a better model based on the residual iterative training of the previous round of models. On this basis, Extreme Gradient Boosting (XGBoost) employs weighted fusion to average the results of each tree for final output, effectively enhancing model accuracy [24]. [PERSON] et al. [25] estimated the population distribution of Shenzhen in 2019 based on five ensemble learning models, and the XGBoost model achieved the best results. Bagging algorithms combine results from multiple learners through averaging or voting to obtain predictive results [26]. As a typical bagging algorithm, random forest (RF) is widely used in population spatialization, and it possesses several advantages compared to other algorithms. It features a more flexible and stable framework. Random Forest integrates predictions from multiple decision trees, with each tree learning from the data in a different way, thereby reducing the risk of overfitting and improving the overall model's generalization ability. This leads to more accurate predictions, helps avoid overfitting, and exhibits higher tolerance to outliers and noise [27]. Population spatialization studiesoften involve various types of data, encompassing a large number of features. Random Forest's flexible and stable framework allows it to effectively handle high dimensional feature spaces, enabling the model to thoroughly consider the impact of different data. Furthermore, since each decision tree is trained independently, Random Forest inherently benefits from parallelization, which accelerates the model training process. This capability is useful in efficiently handling the extensive geographical, social, and economic data involved in the study. [PERSON] et al. [28] utilized the random forest model in regions such as Vietnam, Cambodia, and Kenya to generate high-precision population grid data with 100 m resolution. [PERSON] et al. [29], based on 25 m nighttime light (NTL) data and point of interest (POI) data captured by the International Space Station (ISS), proposed a population spatialization approach for constructing high-resolution urban population distribution data using the random forest method. [PERSON] et al. [30] utilized the random forest model and integrated POI data and multi-source remote sensing data to map China's 2010 population data to 100 m grids. [PERSON] et al. [31] integrated POI data and other multi-source data, and used the random forest algorithm to conduct refined mapping of the population of Zhengzhou City at three scales: 50 m, 300 m, and 500 m, achieving excellent spatialization results. Taking the spatialization of population in Beijing as an example, [PERSON] et al. [32] compared and analyzed different methods, such as RF, MLR, XGBoost, support vector machine (SVM), back propagation neural network (BPNN), and least absolute shrinkage and selection operator (LASSO), and the results show that RF is superior to other methods. The neural network is a mathematical model that emulates the structure and function of biological neural networks, often used to model complex relationships between inputs and outputs. Although neural networks have made initial attempts and achieved certain effects in population spatialization, their interpretability is limited, and the generalization of the model requires further testing and evaluation [33; 34]. Additionally, methods based on neural networks needs end-to-end mapping training, and obtaining population data at a fine grid scale is challenging; this paper does not delve into a detailed exploration of such methods.