Abstract: Poverty is a major social issue commonly faced by developing countries. To address the problem of long-term poverty data gaps, this study proposes a method for constructing a comparable wealth index (CWI) based on household survey data, which is used to represent poverty level. On this basis, a poverty estimation method is proposed,which integrates multi-source spatiotemporal features and a random forest algorithm. The method utilizes data such as nighttime light remote sensing, roads, land cover, digital elevation models, and flood inundation zones to estimate poverty levels in Bangladesh from 2014 to 2021. Experimental results show that the proposed CWI construction method and poverty estimation approach are feasible, with an R² value of 0.88 for the poverty estimation model, indicating high accuracy. The findings can serve as a reference for poverty estimation and analysis in other developing countries.

Key words: poverty level, comparable wealth index, random forest, multi-source geospatial data