A study on the refined population spatialization method integrating multi-source data: A case study of Yuxi city.

ABSTRACT: The precise assessment of earthquake disasters requires high accuracy in population spatialization data. With the rapid development of technologies such as remote sensing and social sensing (social perception), and the growing availability of large-scale geographic data, high-resolution remote sensing data and vast geographical spatial data provide new opportunities for inferring more accurate population data. This paper proposes a method that integrates social perception big data, building attribute data, and multi-source remote sensing data. Using Random Forest, XGBoost, LGBM, Gradient Boosting, AdaBoost, and CatBoost models as base learners, and RidgeCV regression as the secondary learner, a higher-performing Stacking ensemble learning prediction model is constructed. The method system

SUBMITTER: Zhang F 

PROVIDER: S-EPMC12829872 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature