This AI Research Review Explores the Integration of Satellite Imagery and Deep Learning for Measuring Asset-Based Poverty

Researchers from Lund University and Halmstad University conducted a review on explainable AI in poverty estimation through satellite imagery and deep machine learning. Emphasizing transparency, interpretability, and domain knowledge, the analysis of 32 papers reveals that these crucial elements in explainable machine learning exhibit variability and fall short of fully meeting the demands for scientific insights and discoveries in poverty and welfare.

The study finds variability in the status of these core elements by analyzing 32 papers that predict poverty/wealth, using survey data for ground truth, applying it to urban and rural settings, and involving deep neural networks. It argues that the current state does not meet scientific requirements for insights into poverty and welfare. The review underscores the significance of explainability for wider dissemination and acceptance within the development community.

The introduction addresses challenges in identifying vulnerable communities and understanding poverty determinants, citing information gaps and limitations of household surveys. It highlights the potential of deep machine learning and satellite imagery in overcoming these challenges and emphasizing the need for explainability, transparency, interpretability, and domain knowledge in the scientific process, evaluating the status of explainable machine learning in predicting poverty/wealth using survey data, satellite images, and deep neural networks. The goal is to enhance wider dissemination and acceptance within the development community.

Conducting an integrative literature review, the study analyzes 32 studies meeting specific criteria in poverty prediction, survey data, satellite imagery, and deep neural networks. The use of attribution maps in explaining deep-learning imaging models is discussed, and the study assesses model properties for interpretability. The review aims to provide an overview of explainability in the reviewed papers and assess their potential contribution to new knowledge in poverty prediction.

The review of papers reveals varied status in the core elements of explainable machine learning—transparency, interpretability, and domain knowledge—falling short of scientific requirements. Interpretability and explainability are weak, with limited efforts to interpret models or explain predictive data. Domain knowledge is commonly used in feature-based models for selection but less so in other aspects. Experimental results highlight insights, such as modeling the limitations of wealth indices and the impact of low-resolution satellite images. One paper stands out for its strong hypothesis and positive evaluation of domain knowledge.

In the poverty, machine learning, and satellite imagery domain, the status of transparency, interpretability, and domain knowledge in explainable machine learning approaches varies and falls short of scientific requirements. Explainability, crucial for wider dissemination in the development community, surpasses mere interpretability. Transparency in reviewed papers is mixed, with some well-documented and others lacking reproducibility. Weaknesses in interpretability and explainability persist, as few researchers interpret models or explain predictive data. While domain knowledge is common in feature-based models for selection, it is not widely applied in other modeling aspects. Sorting and ranking among impact features is an important future research direction.

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