[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-113046-en":3,"doc-seo-113046-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},113046,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Poverty from Space - Using High Resolution Satellite Imagery for Estimating Economic Well-being","Can features extracted from high spatial resolution satellite imagery accurately estimate poverty and economic well-being? The study extracts object and texture features from satellite images of Sri Lanka to estimate poverty rates and average expected log consumption for 1,291 administrative units. Building counts and density, building shadows, cars, roads, agriculture type, roof material, and spectral texture cues feed a linear regression model. Results explain 49–61% of consumption variation and 37–62% for poverty.","Pub lic Disclosure Authorized Pub lic Disclosure Authorized  \nThe World Bank Economic Review, 36(2), 2022, 382–412  \n[https://doi.org10.1093/wber/lhab015](https://doi.org10.1093/wber/lhab015)  \nArticle  \nPoverty from Space: Using High Resolution Satellite Imagery for Estimating Economic Well-being  \nRyan Engstrom, Jonathan Hersh, and David Newhouse  \nAbstract  \nCan features extracted from high spatial resolution satellite imagery accurately estimate poverty and economic well-being? The present study investigates this question by extracting both object and texture features from satellite images of Sri Lanka. These features are used to estimate poverty rates and average expected log consumption taken from small-area estimates derived from census data, for 1,291 administrative units. Features extracted include the number and density of buildings, the prevalence of building shadows (proxying building height), the number of cars, length of roads, type of agriculture, roof material, and several texture and spectral features. A linear regression model explains between 49 and 61 percent of the variation in average expected log consumption, and between 37 and 62 percent for poverty rates. Estimates remain accurate throughout the consumption distribution, and when extrapolating predictions into adjacent areas, although performance falls when using fewer households to calculate estimates of poverty and welfare.  \nJEL classification: I32, C50  \nKeywords: poverty estimation, satellite imagery, machine learning, big data, inequality  \nRyan Engstrom is an associate professor of geography at George Washington University in Washington, DC; his email address is [rengstro@gwu.edu. Jonathan Hersh](rengstro@gwu.edu. Jonathan Hersh) (corresponding author) is an assistant professor of economics and management science at Chapman University in Orange, CA, and may be [reached at hersh@chapman.edu](reached at hersh@chapman.edu); David Newhouse is a Senior Economist at the Poverty and Equity Global Practice at the World Bank. His email address [is dnewhouse@worldbank.org](is dnewhouse@worldbank.org).  \nThis project benefited greatly from the comments of two anonymous referees and discussions with Sarah Antos, Ana Areias, Marianne Baxter, Sam Bazzi, Azer Bestavros, Jacob Bien, Kristen Butcher, John Byers, Pedro Conceição, Francisco Ferreira, Ray Fisman, Michael Gechter, Alex Guzey, Klaus-Peter Hellwig, Kristen Himelein, Selim Jahan, Matthew Kahn, Tariq Khokhar, Kala Krishna, Hannes Mueller, Trevor Monroe, Dilip Mookherjee, Vivian Peng, Pierre Perron, Hashem Pesaran, Bruno Sánchez-Andrade Nuño, Kiwako Sakamoto, Jacob Shapiro, David Shor, Benjamin Stewart, Andrew Whitby, Nat Wilcox, Nobuo Yoshida, and seminar participants at Boston University, Chapman University, University of Southern California, Penn State, Princeton University, UNDP, The World Bank, and the Department of Census and Statistics of Sri Lanka. All remaining errors in this paper remain the sole responsibility of the authors. Sarah Antos, Benjamin Stewart, and Andrew Copenhaver provided assistance with texture feature classification. Object imagery classification was assisted by James Crawford, Jeff Stein, and Nitin Panjwani at Orbital Insight, and Nick Hubing, Jacqlyn Ducharme, and Chris Loweat Land Info, who also oversaw imagery pre-processing. Hafiz Zainudeen helped validate roof classifications in Colombo. Colleen Ditmars and her team at DigitalGlobe facilitated imagery acquisition, Dung Doan and Dilhanie Deepawansa developed and shared the census-based poverty estimates, and the authors thank Dr. Amare Satharasinghe for authorizing the use of the Sri Lankan census data. Liang Xu and Cady Stringer provided research assistance. Zubair Bhatti, Benu Bidani, Christina Malmberg-Calvo, Adarsh Desai, Nelly Obias, Dhusynanth Raju, Martin Rama, and Ana Revenga provided additional support and encouragement. The authors gratefully acknowledge financial support from the Strategic Research Program and ","cbCaiuEuk3Z9PWNp","https://ap.wps.com/l/cbCaiuEuk3Z9PWNp","pdf",2336848,1,31,"English","en",105,"# Introduction\n## Data gaps in local poverty estimation\n## Satellite imagery as a supplement\n# Methods and features (HSRI-based)\n## Object and texture feature extraction\n## Regression-based estimation\n# Results\n## Fit across consumption distribution\n## Extrapolation to adjacent areas\n# Limitations and robustness\n## Fewer households for poverty estimation","[{\"question\":\"How does the study estimate poverty and economic well-being from satellite imagery?\",\"answer\":\"It extracts object and texture features from high spatial resolution satellite images and uses them to estimate poverty rates and average expected log consumption across 1,291 administrative units.\"},{\"question\":\"Which satellite-derived features are used in the models?\",\"answer\":\"The features include building counts and density, building shadows as a proxy for height, number of cars, road length, agriculture type, roof material, and additional texture and spectral features.\"},{\"question\":\"How accurate are the estimates, and do they hold across the consumption distribution?\",\"answer\":\"A linear regression model explains 49–61% of variation in average expected log consumption and 37–62% for poverty rates, and estimates remain accurate across the consumption 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does the study estimate poverty and economic well-being from satellite imagery?","Question",{"text":75,"@type":76},"It extracts object and texture features from high spatial resolution satellite images and uses them to estimate poverty rates and average expected log consumption across 1,291 administrative units.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which satellite-derived features are used in the models?",{"text":80,"@type":76},"The features include building counts and density, building shadows as a proxy for height, number of cars, road length, agriculture type, roof material, and additional texture and spectral features.",{"name":82,"@type":73,"acceptedAnswer":83},"How accurate are the estimates, and do they hold across the consumption distribution?",{"text":84,"@type":76},"A linear regression model explains 49–61% of variation in average expected log consumption and 37–62% for poverty rates, and estimates remain accurate across the consumption 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