Review

Using satellite imagery to understand and promote sustainable development

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Science  19 Mar 2021:
Vol. 371, Issue 6535, eabe8628
DOI: 10.1126/science.abe8628

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Satellite monitoring of development

Recent years have witnessed rapid growth in satellite-based approaches to quantifying aspects of land use, especially those monitoring the outcomes of sustainable development programs. Burke et al. reviewed this recent progress with a particular focus on machine-learning approaches and artificial intelligence methods. Drawing on examples mostly from Africa, they conclude that satellite-based methods enhance rather than replace ground-based data collection, and progress depends on a combined approach.

Science, this issue p. eabe8628

Structured Abstract

BACKGROUND

Accurate and comprehensive measurements of a range of sustainable development outcomes are fundamental inputs into both research and policy. For instance, good measures are needed to monitor progress toward sustainability goals and evaluate interventions designed to improve development outcomes. Traditional approaches to measurement of many key outcomes rely on household surveys that are conducted infrequently in many parts of the world and are often of low accuracy. The paucity of ground data stands in contrast to the rapidly growing abundance and quality of satellite imagery. Multiple public and private sensors launched in recent years provide temporal, spatial, and spectral information on changes happening on Earth’s surface.

Here we review a rapidly growing scientific literature that seeks to use this satellite imagery to measure and understand various outcomes related to sustainable development. We pay particular attention to recent approaches that use methods from artificial intelligence to extract information from images, as these methods typically outperform earlier approaches and enable new insights. Our focus is on settings and applications where humans themselves, or what they produce, are the outcome of interest and on where these outcomes are being measured using satellite imagery.

ADVANCES

We describe and synthesize the variety of approaches that have been used to extract information from satellite imagery, with particular attention given to recent machine learning–based approaches and settings in which training data are limited or noisy. We then quantitatively assess predictive performance of these approaches in the domains of smallholder agriculture, economic livelihoods, population, and informal settlements. We show that satellite-based performance in predicting these outcomes is reasonably strong and improving. Performance improvements have come through a combination of more numerous and accurate training data, more abundant and higher-quality imagery, and creative application of advances in computer vision to satellite inputs and sustainability outcomes. Further, our analyses suggest that reported model performance likely understates true performance in many settings, given the noisy data on which predictions are evaluated and the types of noise typically observed in sustainability applications. For multiple outcomes of interest, satellite-based estimates can now equal or exceed the accuracy of traditional approaches to outcome measurement. We describe multiple methods through which the true performance of satellite-based approaches can be better understood.

Integration of satellite-based sustainability measurements into research has been broad, and we describe applications in agriculture, fisheries, health, and economics. Documented uses of these measurements in public-sector decision-making are rarer, which we attribute in part to the novelty of the approaches, their lack of interpretability, and the potential benefits to some policy-makers of not having certain outcomes be measured.

OUTLOOK

The largest constraint to satellite-based model performance is now training data rather than imagery. While imagery has become abundant, the scarcity and frequent unreliability of ground data make both training and validation of satellite-based models difficult. Expanding the quantity and quality of such data will quickly accelerate progress in this field. Other opportunities for advancement include improvements in model interpretability, fusion of satellites with other nontraditional data that provide complementary information, and more-rigorous evaluation of satellite-based approaches (relative to available alternatives) in the context of specific use cases.

Nevertheless, despite the current and future promise of satellite-based approaches, we argue that these approaches will amplify rather than replace existing ground-based data collection efforts in most settings. Many outcomes of interest will likely never be accurately estimated with satellites; for outcomes where satellites do have predictive power, high-quality local training data can nearly always improve model performance.

Increasing collection of satellite imagery can help measure livelihood outcomes in areas where ground data are sparse.

(Left) Interval between nationally representative economic surveys over the past three decades shows long lags in many developing countries. (Middle) Recently added public and private satellites have broken the traditional trade-off between temporal and spatial resolution. (Right) Performance in measuring the presence of informal settlements, crop yields on smallholder agricultural plots, and village-level asset wealth. R2, coefficient of determination.

Abstract

Accurate and comprehensive measurements of a range of sustainable development outcomes are fundamental inputs into both research and policy. We synthesize the growing literature that uses satellite imagery to understand these outcomes, with a focus on approaches that combine imagery with machine learning. We quantify the paucity of ground data on key human-related outcomes and the growing abundance and improving resolution (spatial, temporal, and spectral) of satellite imagery. We then review recent machine learning approaches to model-building in the context of scarce and noisy training data, highlighting how this noise often leads to incorrect assessment of model performance. We quantify recent model performance across multiple sustainable development domains, discuss research and policy applications, explore constraints to future progress, and highlight research directions for the field.

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