Introduction
Accurate measurements of poverty and related human livelihood outcomes critically shape the decisions of governments and humanitarian organizations around the world, and the eradication of poverty remains the first of the United Nations Sustainable Development Goals [1]. However, reliable locallevel measurements of economic well-being are rare in many parts of the developing world. Such measurements are typically made with household surveys, which are expensive and time consuming to conduct across broad geographies, and as a result such surveys are conducted infrequently and on limited numbers of households. For example, Uganda (our study country) is one of the best-surveyed countries in Africa, but surveys occur at best every few years, and when they do occur often only survey a few hundred villages across the whole country (Fig. 1). Scaling up these ground-based surveys to cover more regions and more years would likely be prohibitively expensive for most countries in the developing
world [2]. The resulting lack of frequent, reliable local-level information on economic livelihoods hampers the ability of governments and other organizations to target assistance to those who need it and to understand whether such assistance is having its intended effect.
To tackle this data gap, an alternative strategy has been to try to use passively-collected data from non-traditional sources to shed light on local-level economic outcomes. Such work has shown promise in measuring certain indicators of economic livelihoods at local level. For instance, [3] show how features extracted from cell phone data can be used to predict asset wealth in Rwanda, and [4] show how applying NLP techniques to Wikipedia articles can be used to predict asset wealth in multiple developing countries, and [5] show how a transfer learning approach that uses coarse information from nighttime satellite images to extract features from daytime high-resolution imagery can also predict asset wealth variation across multiple African countries.
These existing approaches to using non-traditional data are promising, given that they are inexpensive and inherently scalable, but they face two main challenges that inhibit their broader adoption by policymakers. The first is the outcome being measured. While measures of asset ownership are thought to be relevant metrics for understanding longer-run household well-being [6], official measurement of poverty requires data on consumption expenditure (i.e. the value of all goods consumed by a household over a given period), and existing methods have either not been used to predict consumption data or perform much more poorly when predicting consumption than when predicting other livelihood indicators such as asset wealth [5]. Second, interpretability of model predictions is key for whether policymakers will adopt machine-learning based approaches to livelihoods measurement, and current approaches attempt to maximize predictive performance rather than interpretability. This tradeoff, central to many problems at the interface of machine learning and policy [7], has yet to be navigated in the poverty domain.
Here we demonstrate an interpretable computational framework for predicting local-level consumption expenditure using object detection on high-resolution (30cm) daytime satellite imagery. We focus on Uganda, a country with existing high-quality ground data on consumption where performance benchmark are available. We first train a satellite imagery object detector on a publicly available, global scale
∗Equal Contribution
object detection dataset, called xView [8], which avoids location specific training and provides a more general object detection model. We then apply this detector to high resolution images taken over hundreds of villages across Uganda that were measured in an existing georeferenced household survey, and use extracted counts of detected objects as features in a final prediction of consumption expenditure. We show that not only does our approach substantially outperform previous performance benchmarks on the same task, it also yields features that are immediately and intuitively interpretable to the analyst or policy-maker.