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Photo of Mattias Ohlsson

Mattias Ohlsson

Professor

Photo of Mattias Ohlsson

Human bias and CNNs’ superior insights in satellite based poverty mapping

Author

  • Hamid Sarmadi
  • Ibrahim Wahab
  • Ola Hall
  • Thorsteinn Rögnvaldsson
  • Mattias Ohlsson

Summary, in English

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 poverty estimation diminishes accuracy compared to machine learning involvement, exemplified with the case of Tanzania. Expert defined features exhibited significant overlap and poor interaction when used together in a classifier. Conversely, the CNN-based approach outperformed human experts, demonstrating superior predictive capability with medium-resolution images. These findings highlight the importance of leveraging machine learning explainability methods to identify predictive elements that may be overlooked by human experts. This study advocates for the integration of emerging technologies with traditional methodologies to optimize data collection and analysis of poverty and welfare.

Department/s

  • Department of Human Geography
  • Centre for Environmental and Climate Science (CEC)
  • eSSENCE: The e-Science Collaboration
  • LU Profile Area: Natural and Artificial Cognition

Publishing year

2024-10-02

Language

English

Publication/Series

Scientific Reports

Volume

14

Document type

Journal article

Publisher

Nature Publishing Group

Topic

  • Social Sciences Interdisciplinary
  • Computer Vision and Robotics (Autonomous Systems)

Keywords

  • Convolutional neural networks
  • Domain experts
  • Explainable AI
  • Human bias
  • Satellite imagery
  • Tanzania
  • Welfare estimation

Status

Published

ISBN/ISSN/Other

  • ISSN: 2045-2322