Human bias and CNNs’ superior insights in satellite based poverty mapping
Sarmadi, Hamid ; Wahab, Ibrahim LU ; Hall, Ola LU ; Rögnvaldsson, Thorsteinn and Ohlsson, Mattias LU
(2024)
In Scientific Reports
14.
- 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... (More)
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.
(Less)
- author
- Sarmadi, Hamid
; Wahab, Ibrahim
LU
; Hall, Ola
LU
; Rögnvaldsson, Thorsteinn
and Ohlsson, Mattias
LU
- organization
- publishing date
- 2024-10-02
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Convolutional neural networks, Domain experts, Explainable AI, Human bias, Satellite imagery, Tanzania, Welfare estimation
- in
- Scientific Reports
- volume
- 14
- article number
- 22878
- publisher
- Nature Publishing Group
- external identifiers
-
- pmid:39358399
- scopus:85205527076
- ISSN
- 2045-2322
- DOI
- 10.1038/s41598-024-74150-9
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © The Author(s) 2024.
- id
- 020b902c-d669-4acb-aee7-fc6de93f8464
- date added to LUP
- 2024-10-28 10:43:40
- date last changed
- 2025-07-08 08:48:01
@article{020b902c-d669-4acb-aee7-fc6de93f8464,
abstract = {{<p>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.</p>}},
author = {{Sarmadi, Hamid and Wahab, Ibrahim and Hall, Ola and Rögnvaldsson, Thorsteinn and Ohlsson, Mattias}},
issn = {{2045-2322}},
keywords = {{Convolutional neural networks; Domain experts; Explainable AI; Human bias; Satellite imagery; Tanzania; Welfare estimation}},
language = {{eng}},
month = {{10}},
publisher = {{Nature Publishing Group}},
series = {{Scientific Reports}},
title = {{Human bias and CNNs’ superior insights in satellite based poverty mapping}},
url = {{http://dx.doi.org/10.1038/s41598-024-74150-9}},
doi = {{10.1038/s41598-024-74150-9}},
volume = {{14}},
year = {{2024}},
}