Human bias and CNNs' superior insights in satellite based poverty mapping.

ABSTRACT: Satellite imagery is a potent tool for estimating human wealth and poverty, especially in regions lacking reliable data. This study compares a range of poverty estimation approaches from satellite images, spanning from expert-based to fully machine learning-based methodologies. Human experts ranked clusters from the Tanzania DHS survey using high-resolution satellite images. Then expert-defined features were utilized in a machine learning algorithm to estimate poverty. An explainability method was applied to assess the importance and interaction of these features in poverty prediction. Additionally, a convolutional neural network (CNN) was employed to estimate poverty from medium-resolution satellite images of the same locations. Our analysis indicates that increased human involvement in p

SUBMITTER: Sarmadi H 

PROVIDER: S-EPMC11447245 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature