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In combating poverty, like any fight, it’s good to know the locations of your targets.

That’s why Stanford scholars Marshall BurkeDavid Lobell and Stefano Ermon have spent the past five years leading a team of researchers to home in on an efficient way to find and track impoverished zones across Africa.

The powerful tool they’ve developed combines free, publicly accessible satellite imagery with artificial intelligence to estimate the level of poverty across African villages and changes in their development over time. By analyzing past and current data, the measurement tool could provide helpful information to organizations, government agencies and businesses that deliver services and necessities to the poor.

Details of their undertaking were unveiled in the May 22 issue of Nature Communications.

“Our big motivation is to better develop tools and technologies that allow us to make progress on really important economic issues. And progress is constrained by a lack of ability to measure outcomes,” said Burke, a faculty fellow at the Stanford Institute for Economic Policy Research (SIEPR) and an assistant professor of earth system science in the School of Earth, Energy & Environmental Sciences (Stanford Earth). “Here’s a tool that we think can help.”

Lobell, a senior fellow at SIEPR and a professor of Earth system science at Stanford Earth, says looking back is critical to identifying trends and factors to help people escape from poverty.

“Amazingly, there hasn’t really been any good way to understand how poverty is changing at a local level in Africa,” said Lobell, who is also the director of the Center on Food Security and the Environment and the William Wrigley Fellow at the Stanford Woods Institute for the Environment. “Censuses aren’t frequent enough, and door-to-door surveys rarely return to the same people. If satellites can help us reconstruct a history of poverty, it could open up a lot of room to better understand and alleviate poverty on the continent.”

The measurement tool uses satellite imagery both from the nighttime and daytime. At night, lights are an indicator of development, and during the day, images of human infrastructure such as roads, agriculture, roofing materials, housing structures and waterways, provide characteristics correlated with development.

Then the tool applies the technology of deep learning – computing algorithms that constantly train themselves to detect patterns – to create a model that analyzes the imagery data and forms an index for asset wealth, an economic component commonly used by surveyors to measure household wealth in developing nations.

The researchers tested the measuring tool’s accuracy for about 20,000 African villages that had existing asset wealth data from surveys, dating back to 2009. They found that it performed well in gauging the poverty levels of villages over different periods of time, according to their study.

Here, Burke – who is also a center fellow at the Stanford Woods Institute for the Environment and the Freeman Spogli Institute for International Studies – discusses the making of the tool and its potential to help improve the well-being of the world’s poor.

 

Why are you excited about this new technological resource?

For the first time, this tool demonstrates that we can measure economic progress and understand poverty interventions at both a local level and a broad scale. It works across Africa, across a lot of different years. It works pretty darn well, and it works in a lot of very different types of countries.

 

Can you give examples of how this new tool would be used?

If we want to understand the effectiveness of an anti-poverty program, or if an NGO wants to target a specific product to specific types of individuals, or if a business wants to understand where a market’s growing – all of those require data on economic outcomes. In many parts of the world, we just don’t have those data. Now we’re using data from across sub-Saharan Africa and training these models to take in all the data to measure for specific outcomes.

 

How does this new study build upon your previous work?

Our initial poverty-mapping work, published in 2016, was on five countries using one year of data. It relied on costly, high-resolution imagery at a much smaller, pilot scale. Now this work covers about two dozen countries – about half of the countries in Africa – using many more years of high-dimensional data. This provided underlying training datasets to develop the measurement models and allowed us to validate whether the models are making good poverty estimates.

We’re confident we can apply this technology and this approach to get reliable estimates for all the countries in Africa.

A key difference compared to the earlier work is now we’re using completely publicly available satellite imagery that goes back in time – and it’s free, which I think democratizes this technology. And we’re doing it at a comprehensive, massive spatial scale.

 

How do you use satellite imagery to get poverty estimates?

We’re building on rapid developments in the field of computer science – of deep learning – that have happened in the last five years and that have really transformed how we extract information from images. We’re not telling the machine what to look for in images; instead, we’re just telling it, “Here’s a rich place. Here is a poor place. Figure it out.”

The computer is clearly picking out urban areas, agricultural areas, roads, waterways – features in the landscape that you might think would have some predictive power in being able to separate rich areas from poor areas. The computer says, ‘I found this pattern’ and we can then assign semantic meaning to it.

These broader characteristics, examined at the village level, turn out to be highly related to the average wealth of the households in that region.

 

What’s next?

Now that we have these data, we want to use them to try to learn something about economic development. This tool enables us to address questions we were unable to ask a year ago because now we have local-level measurements of key economic outcomes at broad, spatial scale and over time.

We can evaluate why some places are doing better than other places. We can ask: What do patterns of growth in livelihoods look like? Is most of the variation between countries or within countries? If there’s variation within a country, that already tells us something important about the determinants of growth. It’s probably something going on locally.

I’m an economist, so those are the sorts of questions that get me excited. The technological development is not an end in itself. It’s an enabler for the social science that we want to do.

In addition to Burke, Lobell and Ermon, a professor of computer science, the co-authors of the published study are Christopher Yeh and Anthony Perez, both computer science graduate students and research assistants at the Stanford King Center on Global Development; Anne Driscoll, a research data analyst, and George Azzari, an affiliated scholar, both at the Center on Food Security and the Environment at Stanford; and Zhongyi Tang, a former research data analyst at the King Center. This research was supported by the Data for Development initiative at the Stanford King Center on Global Development and the USAID Bureau of Food Security. To read all stories about Stanford science, subscribe to the biweekly Stanford Science Digest.

Media Contacts

Adam Gorlick, Stanford Institute for Economic Policy Research: (650) 724-0614, agorlick@stanford.edu

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A new tool combines publicly accessible satellite imagery with AI to track poverty across African villages over time.

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Wheat is the most important staple crop grown in Australia, and Australia is one of the top wheat exporting countries globally. Timely and reliable wheat yield prediction in Australia is important for regional and global food security. Prior studies use either climate data, or satellite data, or a combination of these two to build empirical models to predict crop yield. However, though the performance of yield prediction using empirical methods is improved by combining the use of climate and satellite data, the contributions from different data sources are still not clear. In addition, how the regression-based methods compare with various machine-learning based methods in their performance in yield prediction is also not well understood and needs in-depth investigation. This work integrated various sources of data to predict wheat yield across Australia from 2000 to 2014 at the statistical division (SD) level. We adopted a well-known regression method (LASSO, as a benchmark) and three mainstream machine learning methods (support vector machine, random forest, and neural network) to build various empirical models for yield prediction. For satellite data, we used the enhanced vegetation index (EVI) from MODIS and solar-induced chlorophyll fluorescence (SIF) from GOME-2 and SCIAMACHY as metrics to approximate crop productivity. The machine-learning based methods outperform the regression method in modeling crop yield. Our results confirm that combining climate and satellite data can achieve high performance of yield prediction at the SD level (R2 ˜ 0.75). The satellite data track crop growth condition and gradually capture the variability of yield evolving with the growing season, and their contributions to yield prediction usually saturate at the peak of the growing season. Climate data provide extra and unique information beyond what the satellite data have offered for yield prediction, and our empirical modeling work shows the added values of climate variables exist across the whole season, not only at some certain stages. We also find that using EVI as an input can achieve better performance in yield prediction than SIF, primarily due to the large noise in the satellite-based SIF data (i.e. coarse resolution in both space and time). In addition, we also explored the potential for timely wheat yield prediction in Australia, and we can achieve the optimal prediction performance with approximately two-month lead time before wheat maturity. The proposed methodology in this paper can be extended to different crops and different regions for crop yield prediction.

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Agricultural and Forest Meteorology
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Yaping Cai
David Lobell
Andries B.Potgieter, Shaowen Wanga, Jian Peng, Tianfang Xu, Senthold Assen, Yongguang Zhang, Liangzhi You, Bin Peng
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Eradicating hunger and malnutrition is a key development goal of the twenty first century. This paper addresses the problem of optimally identifying seed varieties to reliably increase crop yield within a risk-sensitive decision making framework. Specifically, a novel hierarchical machine learning mechanism for predicting crop yield (the yield of different seed varieties of the same crop) is introduced. This prediction mechanism is then integrated with a weather forecasting model and three different approaches for decision making under uncertainty to select seed varieties for planting so as to balance yield maximization and risk. The model was applied to the problem of soybean variety selection given in the 2016 Syngenta Crop Challenge. The prediction model achieved a median absolute error of 235 kg/ha and thus provides good estimates for input into the decision models. The decision models identified the selection of soybean varieties that appropriately balance yield and risk as a function of the farmer’s risk aversion level. More generally, the models can support farmers in decision making about which seed varieties to plant.

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Huaiyang Zhong, Xiaocheng Li
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Margaret Brandeau
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Accurate prediction of crop yields in developing countries in advance of harvest time is central to preventing famine, improving food security, and sustainable development of agriculture. Existing techniques are expensive and difficult to scale as they require locally collected survey data. Approaches utilizing remotely sensed data, such as satellite imagery, potentially provide a cheap, equally effective alternative. Our work shows promising results in predicting soybean crop yields in Argentina using deep learning techniques. We also achieve satisfactory results with a transfer learning approach to predict Brazil soybean harvests with a smaller amount of data. The motivation for transfer learning is that the success of deep learning models is largely dependent on abundant ground truth training data. Successful crop yield prediction with deep learning in regions with little training data relies on the ability to fine-tune pre-trained models.

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Anna Wang
Caelin Tran
Nikhil Desai
David Lobell
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Crop type mapping at the field level is necessary for a variety of applications in agricultural monitoring and food security. As remote sensing imagery continues to increase in spatial and temporal resolution, it is becoming an increasingly powerful raw input from which to create crop type maps. Still, automated crop type mapping remains constrained by a lack of field-level crop labels for training supervised classification models. In this study, we explore the use of random forests transferred across geographic distance and time and unsupervised methods in conjunction with aggregate crop statistics for crop type mapping in the US Midwest, where we simulated the label-poor setting by depriving the models of labels in various states and years. We validated our methodology using available 30 m spatial resolution crop type labels from the US Department of Agriculture's Cropland Data Layer (CDL). Using Google Earth Engine, we computed Fourier transforms (or harmonic regressions) on the time series of Landsat Surface Reflectance and derived vegetation indices, and extracted the coefficients as features for machine learning models. We found that random forests trained on regions and years similar in growing degree days (GDD) transfer to the target region with accuracies consistently exceeding 80%. Accuracies decrease as differences in GDD expand. Unsupervised Gaussian mixture models (GMM) with class labels derived using county-level crop statistics classify crops less consistently but require no field-level labels for training. GMM achieves over 85% accuracy in states with low crop diversity (Illinois, Iowa, Indiana, Nebraska), but performs sometimes no better than random when high crop diversity interferes with clustering (North Dakota, South Dakota, Wisconsin, Michigan). Under the appropriate conditions, these methods offer options for field-resolution crop type mapping in regions around the world with few or no ground labels.

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Twelve-year-old Lena is growing up poor and malnourished on Chicago’s West Side. She buys Blue Juice and Hot Chips from the corner store on her way to school. She and her classmates can afford the flavoured sugar water and salty starch, but this cheap “food” that fills up her stomach provides no nutritional value. 

Lena is one of over 20 million Americans living in food deserts, places without access to a full-service grocery store within two miles. Yet while Lena buys her Hot Chips, an affluent family nearby uses an online retail platform to order their weekly delivery of fresh, nutritious food – at prices that Lena and her family can’t afford. Despite a surge of technology innovations in food retail, Lena and her family represent a growing number of underserved customers around the world.

Read full story.

 

 

 

 

 

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Food security experts identify government support, policy implementation, private sector engagement and investment in smallholder farmers as keys to Africa’s agricultural future.

Food security experts from the Alliance for a Green Revolution in Africa (AGRA) gathered to discuss transforming food production in Africa at Stanford on Nov. 29. The symposium, hosted by the Center on Food Security and the Environment (FSE) examined the challenges, strategies, and possible solutions for catalyzing and sustaining an inclusive agriculture transformation in Africa. 

Moderator Ertharin Cousin, FSE visiting fellow and previous World Food Programme director with more than 25 years of experience on hunger, food, and resilience strategies, launched the panel by outlining Africa’s plight. “Today some 100 million of the farmers across Sub-Saharan Africa farm less than 2 hectares of land. Some 80 percent of those living in rural areas are poor. More than 30 percent of the rural population is chronically hungry and 35 percent of the under-five-year-olds are stunted. By 2050, the bulk of the world's population growth will take place on the continent. In fact, some project that 1.3 billion will be added to the continent, and Nigeria’s [population] will grow larger than the size of the United States between now and 2050,” Cousin said

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While Africa continues to experience the highest occurrence of food insecurity worldwide, the continent also contains over 60 percent of the worlds uncultivated but fertile land. AGRA formed in 2006 to fulfill the vision that Africa can feed itself and the world. Panelists included Agnes Kalibata, AGRA President and former Minister of Agriculture and Animal Resources of Rwanda; Kanayo F. Nwanze, AGRA board member and immediate past president of the International Fund for Agricultural Development; Usha Barwale Zehr, AGRA board member and Director and Chief Technology Officer of Maharashtra Hybrid Seeds Company Private Limited; and Rajiv Shah, AGRA board member, Rockefeller Foundation President and former Administer of USAID.

Kanayo F. Nwanze stressed the importance of agricultural transformation for Africa’s future. “No country in the world ever transformed itself without going through an agrarian change. No country. Europe, 17th; Japan, 18th century; 19th century was the US, your country; China, 20th century. Why should they be different from Africa? So, first and foremost, we have to have total agricultural transformation,” Nwanze said.

AGRA president, Agnes Kalibata, also spoke to the need for policy implementation and government support in helping drive change. “AGRA as an institution can only do so much. But these governments have the potential and the capacity to reach every corner of their countries. The problem is they are challenged by capacity to do that, by capacity to design proper programs, and by capacity to implement these programs,” Kalibata said.

Expanding on governments' ability to impact and drive change, Usha Barwale Zehr highlighted Asia’s success, specifically with strategic partnerships. “…we've done a lot of talking about public/private partnership. Not so much on the ground on implementing it in a manner, which happened in Asia, for instance, where there was policy, and, most importantly, government will. The government was willing to do whatever it took to make sure that agriculture was transformed at the end of it,” Zehr said.

Beyond government and policy support the panelists also addressed the need for innovation and access to seed technologies. “Why is it that the African farmer and the Indian farmer should not have access to what the American farmer has access to today and reaps benefit from it? …So it's the hybrids, the varieties, the GM technology. Tomorrow it'll be the gene-edited products. And after that we will talk about the satellite-based imaging data that we will use for developing drought-tolerant crops for that very, very small micro environment that existed in the one district in Nigeria,” Zehr said.

"By 2050, who is going to feed Africa? … It's the youth of today. But they're not going to be using the same technologies that exist today. Just think of what IT can do, aggregation, organization of farmer's groups. So, the elements are there. I see the agriculture of tomorrow meeting the challenge – for Africa meeting that challenge is Africa being at the forefront of feeding the world. Africa has to be able to feed itself first. And we have all the opportunities there,” Nwanze said.

This is the first installment of the Global Food Security Symposium series hosted by Stanford University's Center on Food Security and the Environment and generously supported by Zach Nelson and Elizabeth Horn. FSE is a joint initiative of the Stanford Woods Institute for the Environment and the Freeman Spogli Institute for International Studies.

 

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Stanford’s Center on Food Security and the Environment launches new symposium series focused on global food security with panel exploring Africa’s agricultural potential.

Food security experts from the Alliance for a Green Revolution in Africa (AGRA) will gather at Stanford for meetings and a symposium on transforming food production on that continent. R.S.V.P by Nov. 28 for Symposium: Can Africa rise to the challenge of feeding itself in the 21st century? | Nov. 29

Organized by the Center on Food Security and the Environment (FSE), the Nov. 29 symposium is the first in the center’s new Global Food Security Symposium series. Panel members include visiting AGRA board members, who will examine the challenges, strategies, and possible solutions for catalyzing and sustaining an inclusive agriculture transformation in Africa. This symposium marks the third series established by FSE convening thought leaders addressing global food security issues.

Afflicted by conflict, political upheaval, and extreme weather patterns Africa continues to experience the highest occurrence of food insecurity. However, with over 60 percent of the worlds uncultivated but fertile land, there is significant room for improvement. AGRA formed in 2006 to fulfill the vision that Africa can feed itself and the world. As an alliance led by Africans with roots in farming communities across the continent, they work to understand the unique needs of farmers and offer sustainable solutions designed to boost production.

In a region with 27.4 percent of the population currently experiencing food insecurity, creating a sustainable agricultural revolution remains a key solution to improving food security across the area. Moderated by Ertharin Cousin, previous World Food Programme director, with 25 years of experience on hunger, food, and resilience strategies, the panel will explore how an agricultural transformation in Africa can sustain a growing population, relieve hunger, generate jobs, improve social cohesion, and create global exports.

Panel members include:
Ertharin Cousin (moderator), Payne Distinguished Lecturer at the Freeman Spogli Institute for International Studies and Visiting Fellow at the Center on Food Security and the Environment, former US Ambassador to the UN Agencies for Food and Agriculture in Rome.


Agnes Kalibata, the President of AGRA and former Minister of Agriculture and Animal Resources of Rwanda.

Kanayo F. Nwanze, the immediate past president of the International Fund for Agricultural Development (IFAD), winner of the Africa Food Prize in 2016, AGRA board member.

Rajiv Shah, Rockefeller Foundation President, former Administer of USAID (2010-15) where he led bipartisan reform and expansion of US efforts combating global food insecurity. During his previous work at the Gates Foundation he helped launched AGRA.

Usha Barwale Zehr, Director and Chief Technology Officer at Maharashtra – one of India’s largest and most successful multinational seed companies – and AGRA board member.

This is the first installment of the Global Food Security Symposium series hosted by Stanford University's Center on Food Security and the Environment and generously supported by Zach Nelson and Elizabeth Horn. FSE is a joint initiative of the Stanford Woods Institute for the Environment and the Freeman Spogli Institute for International Studies.

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Can Africa rise to the challenge of feeding itself in the 21st century?
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The availability of accurate and reliable information on the location of impoverished zones is surprisingly lacking for much of the world. Applying machine learning to satellite images could identify impoverished regions in Africa.

One of the biggest challenges in providing relief to people living in poverty is locating them. The availability of accurate and reliable information on the location of impoverished zones is surprisingly lacking for much of the world, particularly on the African continent. Aid groups and other international organizations often fill in the gaps with door-to-door surveys, but these can be expensive and time-consuming to conduct.

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In the current issue of Science, Stanford researchers propose an accurate way to identify poverty in areas previously void of valuable survey information. The researchers used machine learning – the science of designing computer algorithms that learn from data – to extract information about poverty from high-resolution satellite imagery. In this case, the researchers built on earlier machine learning methods to find impoverished areas across five African countries.

“We have a limited number of surveys conducted in scattered villages across the African continent, but otherwise we have very little local-level information on poverty,” said study coauthor Marshall Burke, an assistant professor of Earth system science at Stanford and a fellow at the Center on Food Security and the Environment. “At the same time, we collect all sorts of other data in these areas – like satellite imagery – constantly.”

The researchers sought to understand whether high-resolution satellite imagery – an unconventional but readily available data source – could inform estimates of where impoverished people live. The difficulty was that while standard machine learning approaches work best when they can access vast amounts of data, in this case there was little data on poverty to start with.

“There are few places in the world where we can tell the computer with certainty whether the people living there are rich or poor,” said study lead author Neal Jean, a doctoral student in computer science at Stanford’s School of Engineering. “This makes it hard to extract useful information from the huge amount of daytime satellite imagery that’s available.”

Because areas that are brighter at night are usually more developed, the solution involved combining high-resolution daytime imagery with images of the Earth at night. The researchers used the “nightlight” data to identify features in the higher-resolution daytime imagery that are correlated with economic development.

“Without being told what to look for, our machine learning algorithm learned to pick out of the imagery many things that are easily recognizable to humans – things like roads, urban areas and farmland,” said Jean. The researchers then used these features from the daytime imagery to predict village-level wealth, as measured in the available survey data.

They found that this method did a surprisingly good job predicting the distribution of poverty, outperforming existing approaches. These improved poverty maps could help aid organizations and policymakers distribute funds more efficiently and enact and evaluate policies more effectively.

“Our paper demonstrates the power of machine learning in this context,” said study co-author Stefano Ermon, assistant professor of computer science and a fellow by courtesy at the Stanford Woods Institute of the Environment. “And since it’s cheap and scalable – requiring only satellite images – it could be used to map poverty around the world in a very low-cost way.” 

Co-authors of the study, titled “Combining satellite imagery and machine learning to predict poverty,” include Michael Xie from Stanford's Department of Computer Science and David Lobell and W. Matthew Davis from Stanford's School of Earth, Energy and Environmental Sciences and the Center on Food Security and the Environment. For more information, visit the research group's website at: http://sustain.stanford.edu/

 

CONTACTS: 

Neal Jean, School of Engineering: nealjean@stanford.edu, (937) 286-6857

Marshall Burke, School of Earth, Energy and Environmental Sciences: mburke@stanford.edu, (650) 721-2203

Michelle Horton, Center on Food Security and the Environment: mjhorton@stanford.edu, (650) 498-4129

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