metadata: authors: 0: author: Zheng,Zhuo
1: author: Wu,Timothy
2: author: Lee,Richard
3: author: Newhouse,David Locke
4: author: Kilic,Talip
5: author: Burke,Marshall Benajah
6: author: Ermon,Stefano
7: author: Lobell,David B.
count: World
docna: 0: docna: Dynamic, High-Resolution Wealth Measurement
            in Data-Scarce Environments
docty: Policy Research Working Paper
abstracts: cdata!: Accurate and comprehensive measurement
            of household livelihoods is critical for monitoring progress
            toward poverty alleviation and targeting social assistance
            programs for those who most need it. However, the high cost
            of traditional data collection has historically made
            comprehensive measurement a difficult task. This paper
            evaluates alternative satellite-based deep learning
            approaches using detailed household census extracts from
            four African countries to accelerate progress toward
            comprehensive, fine-scale, and dynamic measurement of asset
            wealth at scale. The results indicate that transformer
            architectures solve multiple open measurement problems, by
            providing the most accurate measurement of local-level
            variation in household asset wealth across countries and
            cities, as well as changes in household asset wealth over
            time. Experiments that artificially restrict data
            availability show the model&#x2019;s ability to achieve
            high performance with limited data. The proposed approach
            demonstrates the promise of combining satellite imagery,
            publicly available geo-features, and new deep learning
            architectures for hyperlocal and dynamic measurement of
            wealth in data-scarce environments.
display_title: Dynamic, High-Resolution Wealth
            Measurement in Data-Scarce Environments
listing_relative_url: /research/2025/02/34453694/dynamic-high-resolution-wealth-measurement-data-scarce-environments
topicv3: Poverty Measurement and Analysis m1326941 707
publishtoextweb_dt: 2025-02-06T00:00:00Z
dois: 10.1596/1813-9450-11058
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 13
author_keys: 0: author_key: NA
1: author_key: NA
2: author_key: NA
3: author_key: 450079
4: author_key: 886702
5: author_key: 1227638
6: author_key: NA
7: author_key: NA
model_extractions: Policy Research Working Paper 11058 Dynamic, High-Resolution Wealth Measurement in Data-Scarce Environments _Zhuo Zheng Timothy Wu Richard Lee David Newhouse Talip Kilic Marshall Burke Stefano Ermon David B. Lobell_ Development Economics Development Data Group February 2025
Policy Research Working Paper 11058

# **Abstract**

Accurate and comprehensive measurement of household livelihoods is critical for monitoring progress toward poverty alleviation and targeting social assistance programs for those who most need it. However, the high cost of traditional data collection has historically made comprehensive measurement a difficult task. This paper evaluates alternative satellite-based deep learning approaches using detailed household census extracts from four African countries to accelerate progress toward comprehensive, fine-scale, and dynamic measurement of asset wealth at scale. The results indicate that transformer architectures solve multiple open measurement problems, by providing the most accurate measurement of local-level variation in household asset wealth across countries and cities, as well as changes in household asset wealth over time. Experiments that artificially restrict data availability show the model’s ability to achieve high performance with limited data. The proposed approach demonstrates the promise of combining satellite imagery, publicly available geo-features, and new deep learning architectures for hyperlocal and dynamic measurement of wealth in data-scarce environments.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at dnewhouse@worldbank.org and tkilic@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Dynamic, High-Resolution Wealth Measurement in Data-Scarce Environments

Zhuo Zheng,<sup>_a_</sup> Timothy Wu,<sup>_a_</sup> Richard Lee,<sup>_b,c_</sup> David Newhouse,<sup>_d_</sup> Talip Kilic,<sup>_d_</sup> Marshall Burke,<sup>_c,e_</sup> Stefano Ermon,<sup>_a_</sup> and David B. Lobell<sup>_b,c_</sup> _aDepartment of Computer Science, Stanford University, Stanford, 94305, CA, USA bDepartment of Earth System Science, Stanford University, Stanford, 94305, CA, USA_ _cCenter on Food Security and the Environment, Stanford University, Stanford, 94305, CA, USA dDevelopment Economics Data Group, World Bank Group, Washington DC, 20433, DC, USA eDepartment of Environmental Social Science, Stanford University, Stanford, 94305, CA, USA_

## A R T I C L E I N F O

_Keywords_ : Economic well-being High-resolution Poverty mapping Satellite image Deep learning _JEL codes: C45, I32_ Accurate, up-to-date, and highly resolved measurements of economic well-being are essential for monitoring and achieving international goals of poverty alleviation. These goals include the United Nations’ Sustainable Development Goal 1 of “No Poverty," which is nearing its original 2030 deadline, as well as countless other international and regional poverty targets. Granular estimates of household poverty and wealth are critical for understanding whether these goals are being met, as well as for targeting and evaluating anti-poverty interventions in regions where progress is lagging [6].

Official poverty measurement in low- and middle-income countries has long relied on household surveys, an indispensable but time-consuming tool for livelihood measurement. Given the technical capacity needed for reliable survey measurement and the substantial logistical difficulties in carrying out nationally or sub- nationally representative livelihood surveys, such surveys are often infrequently completed in much of the world, rendering com- prehensive and timely measures of poverty and related outcomes unavailable for many periods in many regions [6, 9]. Meanwhile, survey data are typically based on samples meant to be representative at larger spatial scales and are thus usually inadequate for generating reliable estimates at the village or neighborhood level – the level at which anti-poverty interventions often need to be targeted. Consequently, there is a pressing need for more cost-effective and scalable alternatives to local-level livelihood measurement that can complement and scale existing household survey–based efforts.

In recent years, the abundance of publicly available remote sensing data and recent advances in machine learning have transformed the livelihood measurement landscape, progressively shifting from national censuses and related household surveys to efforts to combine this information with information from satellites and other sensors. Early studies used coarse, publicly available satellite imagery and/or mobile phone data, combined with early machine learning and deep learning architectures, to show how these new sources of information could be used to support broad- scale measurement of wealth and poverty [4, 17, 29]. Subsequent studies introduced further refinements that used publicly available or proprietary geospatial data to improve satellite-based wealth measurement [8, 12, 2]. These advances confirmed that leveraging satellite images and machine learning can be an accurate, inexpensive, and scalable solution to estimate wealth [6, 21, 23].

Here we assemble a large-scale, multi-resolution, and multitemporal wealth dataset using national censuses or extracts obtained from National Statistics Offices and multi-spectral satellite imagery from multiple public and private sensors of varying resolutions. Our dataset comprises over 12 million households in four African countries (Malawi, Mozambique, Burkina Faso, and Madagascar) and, uniquely, contains precisely georeferenced measurements within two Malawian cities as well as repeated measurements of the same locations over time – two features lacking in prior studies.

We use these data to make four contributions relative to earlier work. First, we directly test a new type of deep learning model – specifically, vision transformers – against earlier deep learning architectures based on convolutional neural networks (CNNs) that are common in the literature, as well as against simpler models that use geospatial features and a tabular machine learning approach (XGBoost) for prediction. Specifically, we design a conditioning module that enables our transformer model to handle multi-modal inputs, integrating both satellite imagery and geospatial features simultaneously (see “Methods”). We test models that use satellite imagery from Landsat (30m/pixel), PlanetScope (3m/pixel), and/or SkySat (0.5m/pixel) sensors. We then compare these more sophisticated methods and inputs with simpler methods that rely Page 1 of 11
solely on predefined geospatial features from a range of sources.

These comparisons are important because simpler approaches that rely on publicly available data could be both easier and cheaper to implement at scale, particularly for public organizations interested in their widespread application, and so understanding performance tradeoffs across model architectures and inputs is critical to under- standing how to scale promising new measurement approaches.

Second, a key advantage in our setting is the use of accurate, high-resolution data from national censuses or extracts for model training and evaluation. In contrast to earlier work that relied primarily on publicly available household survey data characterized by spatially imprecise location data and limited household samples, our data cover a much larger set of households in a given location and in some cases are precisely georeferenced. Comparisons against such “gold standard" data allow us to understand whether model prediction errors are a result of inaccurate predictions or noise in the measure of ground truth – an understanding that was often elusive in earlier work in developing countries [6, 29]. In addition, it allows us to consider a wide range of sample sizes to assess the minimum training data requirements for advanced machine learning methods to produce accurate estimates. To quantify the importance of training sample size for performance, we extensively test the extent to which additional training data affects model performance across multiple settings.

Third, our high-resolution census data enables a novel understanding of how satellite imagery and other geospatial data can be used to predict variation in livelihoods within urban areas in Africa – a capability that was again hard to evaluate in previous settings given limited samples and spatial noise in training data. This could be particularly consequential in urban environments that exhibit substantial spatial variability in livelihoods even within small spatial domains. Using comprehensive and precisely georeferenced census data from two cities in Malawi, we are able to train and test models using different resolutions of satellite data, and we find that the models are surprisingly accurate in predicting street- and neighborhood-level variation in wealth within these cities.

Fourth, the censuses and extracts allow us to evaluate whether imagery-based models can make accurate predictions of changes in wealth over time. Previous efforts were again substantially constrained by ground data that did not repeatedly sample the same locations in different surveys [29]. As a result, it remains unclear whether an imagery-based model trained largely to predict spatial variation in wealth or consumption would be able to predict temporal variation, as the latter is typically both smaller and potentially driven by changes that are harder to detect in imagery. Repeated census data from the same locations 10 years apart in Malawi and Mozambique allows us to evaluate whether models can indeed extract information from imagery capable of predicting temporal variation in asset wealth.

# **Results**

## **Performance on prediction of country-level wealth**

For country-level wealth prediction, we train each model on each country individually and conduct country-wise five-fold cross validation for each model. The CNN model uses only Landsat satellite imagery as input. XGBoost utilizes geospatial features (geo-features) either alone or combined with satellite image statistical features. All models were trained to predict the asset wealth index (AWI) [29]. Estimates of AWI were generated and linked to imagery at fine administrative levels in Madagascar, Malawi, and Mozambique. In Burkina Faso, AWI estimates are only available for 334 communes (Table 1). This reduced the effective sample size of the training data in Burkina Faso, as seen in the clumped pattern of survey-measured asset wealth shown in the right panel of Figure 1d. This difference is reflected in the results. As shown in Figure 1a, a naive transformer model that does not condition on geo-features consistently outperforms other models across the Malawi, Mozambique, and Madagascar datasets. In those countries, predictions using the transformer model achieve values of 0.83, 0.70, and 0.62, respectively, when trained on the full census extract. In Burkina Faso, because of the smaller effective sample size, XGBoost using satellite RR<sup>2</sup> imagery and geospatial features achieves the best average performance among the models (62.9% of variation explained). A naive transformer, when using only satellite imagery, remains competitive (57.4% of variation explained). We also limit the number of training samples to 1%, 5%, 10%, 25%, and 50% of the original training dataset to analyze how model performance varies with training sample size. Based on the results from these four countries, we empirically identify 10% as a critical inflection point for model performance, below which the accuracy of the estimates deteriorates rapidly.

Another key factor in reducing training sample collection costs for wealth prediction is the number of households aggregated per sample. To analyze this factor, we randomly sample 10 households per administrative area to construct the training sample, yielding a “10-household” training dataset for each country. We then train a naive transformer model on this “10-household” training set while still evaluating its performance on the original full household test set. The results (Figure 1b) indicate that our transformer models trained with the 10-household data exhibit comparable performance to those trained with data on all households. Using Malawi as an example, the 10-household data only include approximately 23% of all surveyed households. The performance gap between models trained on all households and the 10household sample is only 3 percentage points (82% versus 79%). In contrast, when reducing the number of training samples, the performance gap between models trained on full training samples and those trained on 25% of the samples is as large as 12 percentage points (82% versus 70%).

These results offer significant insights into data-efficient wealth measurement. When at least 10 households are available per image, surveying more enumeration areas takes precedence over surveying additional households within the enumeration area for predicting wealth with our transformer model. Geospatial features, recognized as valuable auxiliary data for improving economic measurement [21], are widely used in wealth prediction. Here we design a conditioning module (see “Methods”) for our transformer model, enabling the efficient fusion of geospatial features and deep visual features. The results, as shown in Figure 1c, suggest that geospatial features significantly improve the ~~-~~ Page 2 of 11
<!-- Start of picture text --> a b #household per EA Country | Malawi (2018) Country | Mozambique (2017)<br>0.84 =m full household<br>= 10-household<br>Country | Malawi (2018) Country| Mozambique (2017) o7<br>08 ———— 06<br>as 4aeaaoeq ed 05<br>ae — aa<br>& * —— - .<br>oaq . 4 gp eran ae 03<br>ft a gee<br>i model ae 0.2<br>mei 5i e=@)“@®= cNNTransformerXGBoostw/ geo-features . JZ‘= 04<br>‘=m XGBoostw/ sat + geo-features # 0.0<br>00 ees ie Ee a Ba cain = ox a wof) ei) as6) aPy osi wssi) wvob) esf) asb) Oaof) @0) wsob)<br>ERERS =S &8 883 iB£5 sacR82“Fg a=8 883 2Sg Country | Madagascar (2018) Country | Burkina Faso (2019)<br>Country | Madagascar (2018) Country | Burkina Faso (2019) os<br>ow 07<br>os<br>:& 06“ o_o- *a5 —_— a _—_ 47 5 : . os04<br>02 ./valyaoeceaerePa aIliebs:- 0302<br>0.0 a z= 04<br>eZes= e et é&¢ 3= :& £88Eeeseo &g¢& 2 := 90 age CO a Sia om CRC a ial<br>“Fe 8 a ~gs “Fe 8 § ~: oir ir ii i i ah Or a Aer aero<br>#traininig data (fraction) #traininig data ({raction) #traininig data (fraction) #traininig data (fraction)<br>Cc Country | Malawi (2018) Country | Mozambique (2017) Country | Madagascar (2018) Country | Burkina Faso (2019)<br>0.8] satellite image only 7<br>mmm satellite image w/ geo-features 1<br>07 4 ny<br>0605 | | i ' n i n |<br>;;<br>'<br>n<br>| |<br>0.3 4 |<br>0.2 |<br>0.4 I<br>0.0<br>AsCTC ae nCrao eene€ W aCC& w es¢ € \ Seh) &gid awae e € wi) oSggso awSge €gg Wstd<br>v i rs Sr sd es Di RPP ph ogi ae a NBS APP AS Pag? Fge av Br BSP eg or<br>d #traininig data (fraction) #traininig data (fraction) #traininig data (fraction) #traininig data (fraction)<br>5 R?: 0.83 Country | Malawi (2018) 7 5 R?: 0.70 Country | Mozambique (2017) ¢ 3 R?: 0.62 Country | Madagascar (2018) 7 2 20.74 Country | Burkina Faso (2019) ¢<br>4 a Al7. ; 7, 2 a, 1 ut 7“!i:<br>5 . wf - 53 “ 5 Ps 5 7 44 «j 3<br>. y S f = 0 ale RE a |<br>ct . og “eg 3B ae ae B-1 ay ae B2 . nae bod<br>= ge 24 a 2 Re a = | ‘<br>“Toe 2 4 an :<br>-2 2 ¢ 4 Survey-measured0 4 asset2 wealth38 4 6 -3 3 72 4Survey-measured0 1 asset2 wealth3 «4 «68 -3 a 7 Survey-measureda) Hy asset wealth1 2 3 “4 4 43 = Survey-measured2 A asset 0wealth 1 2<br><!-- End of picture text -->
<!-- Start of picture text --> Country | Malawi (2018)<br>33°E 36°E<br><!-- End of picture text -->

<!-- Start of picture text --> .<br>,<br>.<br><!-- End of picture text --> <!-- Start of picture text --> y<br>N<br><!-- End of picture text --> <!-- Start of picture text --> i<br>wn<br><!-- End of picture text --> <!-- Start of picture text -->
-1 0 1 2 3 4<br><!-- End of picture text -->

<!-- Start of picture text --> Country | Madagascar (2018)<br><!-- End of picture text -->

<!-- Start of picture text --> a<br><!-- End of picture text --> <!-- Start of picture text --> é<br><!-- End of picture text --> <!-- Start of picture text --> co<br><!-- End of picture text --> <!-- Start of picture text --> S<br><!-- End of picture text --> <!-- Start of picture text --> —2 -1 0 1<br><!-- End of picture text --> <!-- Start of picture text --> Country | Mozambique (2017)<br>32°E 36°E 40°E<br><!-- End of picture text -->

<!-- Start of picture text --> v<br>©<br><!-- End of picture text --> <!-- Start of picture text --> wn<br>st<br>N<br><!-- End of picture text --> <!-- Start of picture text --> —2 -1 0 1 2 3 4<br><!-- End of picture text --> <!-- Start of picture text --> Burkina Faso (2019)<br><!-- End of picture text --> <!-- Start of picture text --> Z<br>wn<br>el<br><!-- End of picture text --> <!-- Start of picture text --> ji<br>+ :<br>oo<br>io<br><!-- End of picture text --> <!-- Start of picture text --> eel<br>N<br><!-- End of picture text --> <!-- Start of picture text --> 3°<br><!-- End of picture text --> <!-- Start of picture text -->
-4 -3 -2 -1 0 1<br><!-- End of picture text -->
<!-- Start of picture text --> a b Country | Malawi (2008-2018)<br>model Country | Malawi (2008-2018) Country | Mozambique (2007-2017) 33°E 36°E<br>0.74 “@ CNN<br>“+ Transformer<br>06 =TM@ ) XGBoost w/ sat-features<br>os ——:——e<br>02 4 wecaeeeeeeenene® Ke —_—_ww= < wn°<br>04 Bat La N<br>:<br>ao / =< ~ pecscnanecses: ed<br>Fe =F = = = Fe = = =<br>cesRBS#5 8og8 Pd85 8=@ 85Seecee a6§ 8=3 8=g<br>#traininig data~ (fraction) Ri a #Htraininig data= (fraction) 8“<br>Cc Country | Malawi (2008-2018) Country | Mozambique (2007-2017) > ae In”<br>0.64 #household per EA et<br>== {ull household<br>"= 10-household<br>05<br>04<br>% o3 d Country | Mozambique: (2007-2017)<br>02 32°E 36°E 40°E<br>o4<br>0.0 t wojo) Res €oo) gesasle) eraelo) aeyole) PrwoCyle) ewWofc) we6.o|o) peasfo) ont xeoojo) aes&le)enwsle) ; r e<br>e #traininig data (fraction) #traininig data (fraction) : " 5 4 . wnR©<br>Country | Malawi (2008-2018) 6 Country | Mozambique (2007-2017)<br>R?: 0.87 4 R?: 0.42 7<br>4 7 5 ¢<br>o2¢¢©Ba ¢¢<br>#3 4 # 7 “<br>s334 £3 Pi t<br>gO3¢ ‘ eeow @$ 2 s if ) wn2<br>E Ra 3 _—: a<br>38-2 7 320‘27ok 4<br>3, ? s3" |. ? 4<br>mA -2 ¢<br>4 ¢<br>-6-6 -4 -2 0 2 4 -3 3 2 7 0 1 2 3 4 5 6<br>‘Survey-measured asset wealth change ‘Survey-measured asset wealth change -—3 —2 -—1 0 1 2 3 4<br><!-- End of picture text -->
<!-- Start of picture text --> Yo<br>4vA<br><!-- End of picture text --> <!-- Start of picture text --> /<br>47.<br><!-- End of picture text --> <!-- Start of picture text --> n<br><!-- End of picture text --> <!-- Start of picture text --> _ #§#83=6Cf<br>-1 0 1 2 3 4<br><!-- End of picture text --> <!-- Start of picture text --> 33.6°E 34°E<br><!-- End of picture text --> <!-- Start of picture text --> 33.6°E 34°E<br>latin ims<br>| BPR al r<br>Wey |<br><!-- End of picture text -->

<!-- Start of picture text --> j<br>r<br>ai<br>: ie 3<br>:<br>Lilongwe : "<br>‘i<br>Blantyre “i i<br>:<br><!-- End of picture text --> <!-- Start of picture text -->
| |<br>i<br>ae<br>||<br><!-- End of picture text -->

<!-- Start of picture text --> is<br><!-- End of picture text --> <!-- Start of picture text --> ae<br><!-- End of picture text -->
countries, the wealth distribution changes are not uniform, suggesting that certain areas are benefiting more from economic growth while others are falling behind. This wealth disparity could provide insights into economic policies, development programs, or external factors such as climate impacts that have influenced these changes.

## **Performance on prediction of city-level wealth**

The Landsat-based wealth prediction models above produced wealth maps with a spatial resolution of 4.8 km. For some applications, such as targeting aid within urban areas, finerresolution wealth maps are of interest. To that end, we utilized household- level census data from two cities to test wealth prediction using high-resolution satellite imagery (PlanetScope and SkySat). Following the same settings with the above two subsections, we evaluate the CNN, transformer, and XGBoost models, as shown in Figure 4a. The CNN and Transformer models consistently perform better than XGBoost (with or without geospatial features) across both cities. This highlights the importance of deep visual representation from high-resolution satellite imagery for city-level wealth prediction, which constitutes the main gap between deep learning–based models and XGBoost. Our transformer model outperforms the CNN by a noticeable margin in Blantyre, especially when utilizing the full dataset, demonstrating greater scalability with increased data while also achieving comparable performance to the CNN in Lilongwe. Across both cities, all models exhibit significant improvements as the training data fraction increases; however, performance gains begin to plateau after approximately 25%-50% of the data, resulting in diminishing gains beyond that threshold.

We compare two kinds of commonly used proprietary highresolution, multi-spectral satellite imagery, i.e., PlanetScope (3m) and SkySat (0.5m), as shown in Figure 4b. The results indicate that SkySat consistently outperforms PlanetScope across various training data fractions in both cities. Both sensors capture 4-band (red, green, blue, and near-infrared) satellite imagery, but they differ in spatial resolution and swath width. This highlights the importance of urban spatial detail in accurately measuring wealth using our transformer model. While PlanetScope demonstrates lower average performance than SkySat, its broader swath width and high revisit frequency yield more comprehensive satellite imagery, facilitating large-scale wealth mapping with commendable accuracy (Figures 4g and 4i). Consequently, SkySat is well-suited for wealth measurement in local areas with high accuracy requirements, and PlanetScope is more suitable for large-scale wealth mapping to obtain macro insights.

While city-level wealth prediction is promising, training a city-level transformer still requires sufficient samples that are expensive to collect. We also evaluate whether integrating geospatial features can reduce the required training samples for city-level wealth prediction. As presented in Figure 4c, we find that unlike for country-level predictions, geospatial features generally reduce model performance. This is because geospatial features are al- ways derived from low-resolution satellite imagery, e.g., Landsat (30m), to achieve global coverage. These low-resolution geospatial features introduce spatial errors into high-resolution satellite image–based wealth prediction. For <u>example, a coarse land cover map can ignore small but important</u> spatial details in urban areas. In this case, our transformer model can learn more accurate wealth representations solely from satellite images, even when trained on varying sample sizes.

Overall, we demonstrate accurate large-scale, city-level wealth mapping in two cities in Malawi, i.e., Lilongwe and Blantyre. Compared to country-level wealth maps at a 4.8km resolution (Figures 4f and 4 h), our 0.3km resolution city-level wealth maps (Figures 4g and 4i) provide an unprecedentedly granular spatial distribution of wealth across these two cities and with strong performance explaining up to 76% of the variation for Lilongwe and up to 67% for Blantyre (Figure 4d).

# **Discussion**

This paper proposes and evaluates the use of a vision transformer architecture to solve multiple open problems pertaining to combining survey and satellite data to produce wealth estimates at fine spatial scales. For cross-sectional wealth predictions, careful evaluations using georeferenced census extracts from four countries show that estimates from the transformer model perform well. When paired with Landsat imagery, R<sup>2</sup> values for transformer models that incorporate geo-features outperform commonly used CNN and XGboost models for asset index prediction in all four countries, for all sample sizes considered. Across all countries, accuracy degrades rapidly when using less than 10% of the census extract for training. In Mozambique and Madagascar, estimates produced using transformer models explained approximately 20 to 30 percentage points more of the variation in wealth than estimates produced using XGboost and geo-features. Trans- former models also outperform CNNs in all four countries, by amounts up to approximately 5 percentage points in Madagascar and Mozambique. Incorporating geofeatures into the transformer architecture improves performance by 5 to 10 percentage points at small sample sizes in Mozambique, Madagascar, and Burkina Faso. Transformer models also perform well when predicting variation within cities at 0.3km scales, achieving up to 0.76 in Lilongwe and 0.67 in Blantyre. However, incorporating geofeatures at this scale reduced performance, because they are RR<sup>2</sup> constructed from lower-resolution Landsat imagery. Finally, the transformer model also generates more accurate estimates than CNNs and XGboost when predicting decadal changes in the asset wealth index in Mozambique and Malawi. Model predictions achieve values of 0.57 in Malawi and 0.42 in Mozambique at fine spatial levels, despite the lack of available geo-features. This is a large improvement over the 0.15 to 0.17 RR<sup>2</sup> reported by [29] in comparable settings, and demonstrates the feasibility of combining transformer models with imagery to estimate wealth RR<sup>2</sup> changes at granular levels, given sufficient training data.

The results point to the benefits of applying transformer models that incorporate geospatial features to generate highresolution predictions of asset wealth. This in turn underscores the importance of developing tools, documentation, and training materials to make estimation feasible for national statistics offices, international organizations, and other data providers. In addition, developing and evaluating methods for estimating the uncertainty associated with predictions is crucial to facilitate implementation.

The results also highlight the importance of having access to a Page 7 of 11
critical mass of training data to estimate predictive models. In general, when the number of images we used to train the model fell below 10% of the population, predictive performance deteriorated rapidly. However, the results were far more robust to restricting the size of the sample used to generate the training data labels. Furthermore, prediction accuracy remained high in Burkina Faso, despite a reduced effective sample size of the training data due to linking satellite images to survey data at a much higher geographic level. Future work could investigate methods to further improve performance when training transformer models using the types of small samples typically collected for household surveys.

Finally, the results demonstrate the potential of using transformer models to predict changes in wealth and household wellbeing more generally. Future work can examine the extent to which the parameters in change models are stable across time and/or space. This could point the way toward the use of geospatial data to generate approximate micro estimates of welfare change in settings where survey data are unavailable.

# **Methods**

We describe the details of our large-scale, multi-resolution, and multi-temporal wealth dataset, wealth and its change prediction approaches, and evaluation methods.

## **Multi-resolution and multi-temporal wealth dataset.** We

utilize data from four low-income countries (Malawi, Mozambique, Burkina Faso, and Madagascar) and two cities in Malawi as study areas in Africa. These countries were selected due to the availability of location identifiers in available census extracts. This allows us to pinpoint models on the most comprehensive scale to date, to tune a general model to specific countries and even cities, and to robustly simulate the impact of data scarcity on model performance across three spatiotemporal scenarios: country-level wealth level prediction, decadal country-level wealth change prediction, and city-level wealth level prediction.

**Asset wealth index (AWI).** We construct the asset wealth index using data from the national census questionnaire. We utilize full census data in Mozambique and Madagascar, and census extracts in Malawi and Burkina Faso, resulting in a total dataset comprising over 12 million households across four countries, with more than 700,000 households represented in each country’s dataset. In contrast, previous studies [29, 23] leveraging DHS data included approximately 500,000 households across 23 African countries, while LSMS data measured about 9,000 households across five countries. Table 1 provides a full description of the size of the datasets.

From the census questionnaire, we rank seven housing characteristics (housing type, wall material, roof material, floor material, water source, toilet type, and energy source) on a scale of 1 to 5. Additionally, we assess the presence of six assets (radio, television, landline, car, motorbike, and bicycle) using a binary classification (ownership/non-ownership). This data is then standardized and used to construct a principal components analysis (PCA) model, from which the first principal component was extracted as the asset wealth index [13, 25, 29]. Asset wealth index labels are then aggregated from the household to the

**Table 1**

National census data details.

|Country|#Images # Admin areas # Households Surveyed|
|---|---|
|Malawi (2008)|3,432<br>12,412<br>572,764|
|Malawi (2018)|3,432<br>18,700<br>796,925|
|Mozambique (2007)|37,325<br>45,244<br>4,797,372|
|Mozambique (2017)|37,325<br>67,218<br>6,119,847|
|Burkina Faso (2019)|13,142<br>334<br>875,872|
|Madagascar (2018)|31,182<br>14,328<br>4,518,322|

administrative area level, and then each pixel of the images is labeled based on the administrative area to which they belong. Finally, we average the pixel-wise asset wealth index map to obtain a scalar value as the ground truth for each image. Since a different PCA is constructed for each country, a value of 0 in one country does not correspond to the same level of wealth as a 0 in another country.

**Satellite imagery.** For country-level wealth and its change prediction, we collected daylight Landsat (30m/pixel) satellite imagery for Malawi, Mozambique, Madagascar, and Burkina Faso, where Malawi and Mozambique have bitemporal image pairs. Our Landsat imagery dataset was constructed using a 3-year median of cloud-free pixels, centered around the census year for each country. For countries with two census periods, imagery from Landsat 5 and Landsat 7 was used for the earlier census, while Landsat 8 was utilized for the more recent census. Each Landsat image has a fixed size of 150 × 150 pixels, resulting in each image covering 20.25km<sup>2</sup> (4.5 × 4.5km<sup>2</sup> ). These images have six bands that are red, green, blue, near-infrared, short-wave infrared 1, and short-wave infrared 2. For city-level wealth prediction, we collect PlanetScope (3m/pixel) and SkySat (0.5m/pixel) multispectral satellite imagery to cover each administrative area in Lilongwe and Blantyre. Based on the average size of administrative areas, we empirically define the size of each grid as 0.3 × 0.3km<sup>2</sup> , which results in each PlanetScope image with 100 × 100 pixels and each SkySat image with 600 × 600 pixels. Despite using 2018 household-level census data, we utilized available PlanetScope and SkySat imagery acquired in April 2023. These images contain red, green, blue, and near-infrared bands.

**Geospatial features.** In addition, we supplement satellite imagery with publicly available processed geospatial features, which we refer to as _geo-features_ . These geo-features capture population, developmental, and environmental statistics. These features are population structure [18], population density [28], annual rainfall [1], minimum and maximum temperature [1], nighttime lights [3, 11], terra net primary product [10], aqua net primary production [24], cellphone tower count [22], impervious surface change year [15], land cover type [5], GHSL [26], building counts [27], building areas [27], soil pH [16], and soil organic carbon [16]. A visual representation of one datapoint is provided in Figure 5; note that final asset wealth labels are combined into a single scalar value.

## **Training wealth measurement models**

The comparisons include a tree-based model, namely extreme gradient boosting (XGBoost) [7] and two advanced deep learning models (convolutional neural networks and transformers based on an encoder-linear architecture [29]. Based on empirical and Page 8 of 11
<!-- Start of picture text --> » BLUE oeep x GREEN aesae RED x=e NIR ‘oeee<br>®= cacay a“% oeen—*:wsee © ©en“ asebaz<br>wea CIM be36 ear % = 0> oaeas<br>ay c = se cos roa cee O92es eae a a ee peet<br>. SWIRL ee) SWIRZ a population “pop _density ~ cee<br>= * ek OF we,<br>e2 zy » ee“ ino ms Hee<br>1221m. <7 iMet a 704 ©19) eo20 wee<br>rea eo ke ed «a i<br>© ry pers 139 e ae = 439 2 ey ae pry > 2 rey 0 ee 7<br>r asim tmmn tmmx 2208 nightlights<br>= row was ase pass<br>o amr amen pate 7 ey<br>499 2989 a bears 8 +2522 :c0 ws<br>5 aqua 3 . terra . ._cellid_count “*" change_year_inde<br>=.° 1 «-*# a « woe 9Beees fe:ere.ioy eeaeOn<br>€ x we 139 2 ts 3 Be Fey 1 3 30 ate aes ae ? 3 £0 ue ><br>land_cover 20 smod_code - building area «2, building count | ~~<br>»=.® =‘a. Pax& a x<9“ sePray“a -ies eee:<br>weoat rere E» a 5+ sollont meewee<br>ay ae at et pace<br>pH oo soc «~~ asset_index_full Pe . asset _index_10 |>::<br>2. 4 “ a + 27—<br>® a » « . @ Ss<br>189 orn ae. eam re<br>© Py we ree) 4 e 36 ay ries 2 Ey we ae ose 2 Ey Et as<br><!-- End of picture text -->
# **Acknowledgments**

We thank Lina Cardona, Carlos Da Maia, Francis Mulangu, Mario Negre, Soudiki Soubeiga, and Michael Weber for their help obtaining data; Haishan Fu, Olivier Dupriez, Craig Hammer, and Jed Friedman for their support; and Brian Amaro, Nahum Maru, and Rohan Sikand for help with initial analysis. This project was partially funded by the Knowledge for Change Program’s Phase IV-funded programmatic research project “Understanding Trends in Sub-National Differences in Economic Well-Being in Low- and Middle-Income Countries" and by the Keck Foundation.

## **Author Contributions**

DN, TK, MB, SE, and DL conceived of the project and designed analysis; RL processed data; ZZ and TW led the analysis; ZZ, DL, DN, and MB wrote the paper.

## **Code and Data Availability**

Code to conduct analysis and generate figures is available at <u>https://github.com/Z-Zheng/dynamic_highres_poverty. We do not</u> currently have permission from country national statistics offices to share the household level data or image-level labels.

Page 10 of 11