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Using publicly available satellite imagery and deep learning to understand economic well-being in Africa

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  1. Delprato, Marcos & Frola, Alessia & Antequera, Germán, 2022. "Indigenous and non-Indigenous proficiency gaps for out-of-school and in-school populations: A machine learning approach," International Journal of Educational Development, Elsevier, vol. 93(C).
  2. Patrick Lehnert & Michael Niederberger & Uschi Backes-Gellner & Eric Bettinger, 2020. "Proxying Economic Activity with Daytime Satellite Imagery: Filling Data Gaps Across Time and Space," Economics of Education Working Paper Series 0165, University of Zurich, Department of Business Administration (IBW), revised Sep 2022.
  3. Al Kez, Dlzar & Foley, Aoife & Abdul, Zrar Khald & Del Rio, Dylan Furszyfer, 2024. "Energy poverty prediction in the United Kingdom: A machine learning approach," Energy Policy, Elsevier, vol. 184(C).
  4. Piotr Wójcik & Krystian Andruszek, 2022. "Predicting intra‐urban well‐being from space with nonlinear machine learning," Regional Science Policy & Practice, Wiley Blackwell, vol. 14(4), pages 891-913, August.
  5. Merfeld, Joshua D. & Newhouse, David & Weber, Michael & Lahiri, Partha, 2022. "Combining Survey and Geospatial Data Can Significantly Improve Gender-Disaggregated Estimates of Labor Market Outcomes," IZA Discussion Papers 15390, Institute of Labor Economics (IZA).
  6. John D. Huber & Laura Mayoral, 2024. "Economic Development in Pixels: The Limitations of Nightlights and New Spatially Disaggregated Measures of Consumption and Poverty," Working Papers 1433, Barcelona School of Economics.
  7. Donghyun Ahn & Jeasurk Yang & Meeyoung Cha & Hyunjoo Yang & Jihee Kim & Sangyoon Park & Sungwon Han & Eunji Lee & Susang Lee & Sungwon Park, 2023. "A human-machine collaborative approach measures economic development using satellite imagery," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
  8. Ola Hall & Mattias Ohlsson & Thortseinn Rognvaldsson, 2022. "Satellite Image and Machine Learning based Knowledge Extraction in the Poverty and Welfare Domain," Papers 2203.01068, arXiv.org.
  9. Ola Hall & Francis Dompae & Ibrahim Wahab & Fred Mawunyo Dzanku, 2023. "A review of machine learning and satellite imagery for poverty prediction: Implications for development research and applications," Journal of International Development, John Wiley & Sons, Ltd., vol. 35(7), pages 1753-1768, October.
  10. Maryia Bakhtsiyarava & Tim G. Williams & Andrew Verdin & Seth D. Guikema, 2021. "A nonparametric analysis of household-level food insecurity and its determinant factors: exploratory study in Ethiopia and Nigeria," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 13(1), pages 55-70, February.
  11. Douglas Kiarelly Godoy de Araujo, 2023. "gingado: a machine learning library focused on economics and finance," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Data science in central banking: applications and tools, volume 59, Bank for International Settlements.
  12. Meyer, Maximilian & Hulke, Carolin & Kamwi, Jonathan & Kolem, Hannah & Börner, Jan, 2022. "Spatially heterogeneous effects of collective action on environmental dependence in Namibia’s Zambezi region," World Development, Elsevier, vol. 159(C).
  13. Klaus Ackermann & Alexey Chernikov & Nandini Anantharama & Miethy Zaman & Paul A Raschky, 2020. "Object Recognition for Economic Development from Daytime Satellite Imagery," SoDa Laboratories Working Paper Series 2020-02, Monash University, SoDa Laboratories.
  14. Michler, Jeffrey D. & Josephson, Anna & Kilic, Talip & Murray, Siobhan, 2022. "Privacy protection, measurement error, and the integration of remote sensing and socioeconomic survey data," Journal of Development Economics, Elsevier, vol. 158(C).
  15. Robin Jarry & Marc Chaumont & Laure Berti-Équille & Gérard Subsol, 2023. "Comparing spatial and spatio-temporal paradigms to estimate the evolution of socio-economical indicators from satellite images," Post-Print hal-04268542, HAL.
  16. Linsenmeier, Manuel, 2021. "Temperature variability and long-run economic development," SocArXiv xvucn, Center for Open Science.
  17. Barzin,Samira & Avner,Paolo & Maruyama Rentschler,Jun Erik & O’Clery,Neave, 2022. "Where Are All the Jobs ? A Machine Learning Approach for High Resolution Urban Employment Prediction inDeveloping Countries," Policy Research Working Paper Series 9979, The World Bank.
  18. Yin, Hui & Zhou, Kaile, 2022. "Performance evaluation of China's photovoltaic poverty alleviation project using machine learning and satellite images," Utilities Policy, Elsevier, vol. 76(C).
  19. Jung, Woojin, 2023. "Mapping community development aid: Spatial analysis in Myanmar," World Development, Elsevier, vol. 164(C).
  20. He, Jing & Ren, Fu & Weibel, Robert & Fu, Cheng, 2023. "The effect of large scale photovoltaic-based projects on poverty reduction: Empirical evidence from China," Renewable Energy, Elsevier, vol. 218(C).
  21. Hu, Ting & Wang, Ting & Yan, Qingyun & Chen, Tiexi & Jin, Shuanggen & Hu, Jun, 2022. "Modeling the spatiotemporal dynamics of global electric power consumption (1992–2019) by utilizing consistent nighttime light data from DMSP-OLS and NPP-VIIRS," Applied Energy, Elsevier, vol. 322(C).
  22. Hannes Mueller & André Groeger & Jonathan Hersh & Andrea Matranga & Joan Serrat, 2021. "Monitoring War Destruction from Space Using Machine Learning," Working Papers 1257, Barcelona School of Economics.
  23. Yi Bao & Zhou Huang & Han Wang & Ganmin Yin & Xiao Zhou & Yong Gao, 2023. "High‐resolution quantification of building stock using multi‐source remote sensing imagery and deep learning," Journal of Industrial Ecology, Yale University, vol. 27(1), pages 350-361, February.
  24. Anders Christensen & Joel Ferguson & Sim'on Ram'irez Amaya, 2022. "Incorporating High-Frequency Weather Data into Consumption Expenditure Predictions," Papers 2211.01406, arXiv.org.
  25. Bolivar, Osmar, 2023. "Evolución de la pobreza en las comunidades de Bolivia entre 2012 y 2022: Un enfoque de machine learning y teledetección [Evolution of poverty in Bolivian communities between 2012 and 2022: A machin," MPRA Paper 118932, University Library of Munich, Germany.
  26. Niall Farrell, 2024. "Small Area Poverty Estimation by Conditional Monte Carlo," Papers WP773, Economic and Social Research Institute (ESRI).
  27. Yujun Zhou & Erin Lentz & Hope Michelson & Chungmann Kim & Kathy Baylis, 2022. "Machine learning for food security: Principles for transparency and usability," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 44(2), pages 893-910, June.
  28. Tanner Regan & Giorgio Chiovelli & Stelios Michalopoulos & Elias Papaioannou, 2023. "Illuminating Africa?," Working Papers 2023-11, The George Washington University, Institute for International Economic Policy.
  29. Newhouse,David Locke & Merfeld,Joshua David & Ramakrishnan,Anusha Pudugramam & Swartz,Tom & Lahiri,Partha, 2022. "Small Area Estimation of Monetary Poverty in Mexico Using Satellite Imagery and Machine Learning," Policy Research Working Paper Series 10175, The World Bank.
  30. Binh Tang & Yanyan Liu & David S. Matteson, 2022. "Predicting poverty with vegetation index," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 44(2), pages 930-945, June.
  31. Guo, Jinyu & Ma, Jinji & Li, Zhengqiang & Hong, Jin, 2022. "Building a top-down method based on machine learning for evaluating energy intensity at a fine scale," Energy, Elsevier, vol. 255(C).
  32. Mukaigawara, Mitsuru & Zhou, Lingxiao & Papadogeorgou, Georgia & Lyall, Jason & Imai, Kosuke, 2024. "geocausal: An R Package for Spatio-Temporal Causal Inference," OSF Preprints 5kc6f, Center for Open Science.
  33. Li, Qing & Yu, Shuai & Échevin, Damien & Fan, Min, 2022. "Is poverty predictable with machine learning? A study of DHS data from Kyrgyzstan," Socio-Economic Planning Sciences, Elsevier, vol. 81(C).
  34. Adham Alsharkawi & Mohammad Al-Fetyani & Maha Dawas & Heba Saadeh & Musa Alyaman, 2021. "Poverty Classification Using Machine Learning: The Case of Jordan," Sustainability, MDPI, vol. 13(3), pages 1-16, January.
  35. Hannes Mueller & Andre Groger & Jonathan Hersh & Andrea Matranga & Joan Serrat, 2020. "Monitoring War Destruction from Space: A Machine Learning Approach," Papers 2010.05970, arXiv.org, revised Oct 2020.
  36. GIBSON, John & ZHANG, Xiaoxuan & PARK, Albert & YI, Jiang & XI, Li, 2024. "Remotely measuring rural economic activity and poverty : Do we just need better sensors?," CEI Working Paper Series 2023-08, Center for Economic Institutions, Institute of Economic Research, Hitotsubashi University.
  37. Masaki,Takaaki & Newhouse,David Locke & Silwal,Ani Rudra & Bedada,Adane & Engstrom,Ryan, 2020. "Small Area Estimation of Non-Monetary Poverty with Geospatial Data," Policy Research Working Paper Series 9383, The World Bank.
  38. Abbate Nicolás & Gasparini Leonardo & Gluzmann Pablo Alfredo & Montes Rojas Gabriel & Sznaider Iván & Yatche Tobías, 2023. "Ingreso Estructural Por Área Geográfica: una aplicación para Argentina," Asociación Argentina de Economía Política: Working Papers 4622, Asociación Argentina de Economía Política.
  39. Linden McBride & Christopher B. Barrett & Christopher Browne & Leiqiu Hu & Yanyan Liu & David S. Matteson & Ying Sun & Jiaming Wen, 2022. "Predicting poverty and malnutrition for targeting, mapping, monitoring, and early warning," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 44(2), pages 879-892, June.
  40. Soyoka OKAMURA & Yotaro UENO & Toma YAMAGOSHI & Hisaki KONO, 2024. "Revisiting National Institutions and Subnational Development in Africa with New Nighttime Light Data," Discussion papers e-23-008, Graduate School of Economics , Kyoto University.
  41. Imryoung Jeong & Hyunjoo Yang, 2021. "Using maps to predict economic activity," Papers 2112.13850, arXiv.org, revised Apr 2022.
  42. Lee, Kamwoo & Braithwaite, Jeanine, 2022. "High-resolution poverty maps in Sub-Saharan Africa," World Development, Elsevier, vol. 159(C).
  43. Qingyi Wang & Shenhao Wang & Yunhan Zheng & Hongzhou Lin & Xiaohu Zhang & Jinhua Zhao & Joan Walker, 2023. "Deep hybrid model with satellite imagery: how to combine demand modeling and computer vision for behavior analysis?," Papers 2303.04204, arXiv.org, revised Feb 2024.
  44. De Angelis, Paolo & Tuninetti, Marta & Bergamasco, Luca & Calianno, Luca & Asinari, Pietro & Laio, Francesco & Fasano, Matteo, 2021. "Data-driven appraisal of renewable energy potentials for sustainable freshwater production in Africa," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
  45. Ian McCallum & Christopher Conrad Maximillian Kyba & Juan Carlos Laso Bayas & Elena Moltchanova & Matt Cooper & Jesus Crespo Cuaresma & Shonali Pachauri & Linda See & Olga Danylo & Inian Moorthy & Myr, 2022. "Estimating global economic well-being with unlit settlements," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
  46. Adel Daoud & Felipe Jordán & Makkunda Sharma & Fredrik Johansson & Devdatt Dubhashi & Sourabh Paul & Subhashis Banerjee, 2023. "Using Satellite Images and Deep Learning to Measure Health and Living Standards in India," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 167(1), pages 475-505, June.
  47. Li, Kuanhong & Wang, Linping & Wang, Lianhui, 2024. "Consumption as the catalyst: Analyzing rural power infrastructure and agricultural growth through panel threshold regression and data-driven prediction," Applied Energy, Elsevier, vol. 365(C).
  48. Guanghua Chi & Han Fang & Sourav Chatterjee & Joshua E. Blumenstock, 2022. "Microestimates of wealth for all low- and middle-income countries," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 119(3), pages 2113658119-, January.
  49. Wang, Qingyi & Wang, Shenhao & Zheng, Yunhan & Lin, Hongzhou & Zhang, Xiaohu & Zhao, Jinhua & Walker, Joan, 2024. "Deep hybrid model with satellite imagery: How to combine demand modeling and computer vision for travel behavior analysis?," Transportation Research Part B: Methodological, Elsevier, vol. 179(C).
  50. Martina Jakob & Sebastian Heinrich, 2023. "Measuring Human Capital with Social Media Data and Machine Learning," University of Bern Social Sciences Working Papers 46, University of Bern, Department of Social Sciences.
  51. Nadine Bachmann & Shailesh Tripathi & Manuel Brunner & Herbert Jodlbauer, 2022. "The Contribution of Data-Driven Technologies in Achieving the Sustainable Development Goals," Sustainability, MDPI, vol. 14(5), pages 1-33, February.
  52. Christopher B. Barrett, 2021. "Overcoming Global Food Security Challenges through Science and Solidarity," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(2), pages 422-447, March.
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