{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T11:16:37Z","timestamp":1778152597228,"version":"3.51.4"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T00:00:00Z","timestamp":1778112000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T00:00:00Z","timestamp":1778112000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-026-21359-7","type":"journal-article","created":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T11:02:19Z","timestamp":1778151739000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting wealth score from remote sensing satellite images and household survey data using deep transfer learning"],"prefix":"10.1007","volume":"85","author":[{"given":"Shailesh","family":"Tiwari","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pratibha","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shashank","family":"Shekhar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,7]]},"reference":[{"key":"21359_CR1","doi-asserted-by":"crossref","unstructured":"Sustainable Development Goals (2022) Goal 1: End poverty in all its forms everywhere, United Nations. Sustainable Development Goals: 17 Goals to Transform Our World. Available at: https:\/\/www.un.org\/sustainabledevelopment\/poverty\/, last accessed on Jan","DOI":"10.1007\/978-3-030-38815-7_1"},{"key":"21359_CR2","doi-asserted-by":"crossref","unstructured":"Nischal KN, Radhakrishnan R, Mehta S, Chandani S (2015) Correlating night-time satellite images with poverty and other census data of India and estimating future trends. In Proceedings of the Second ACM IKDD Conference on Data Sciences, pp. 75\u201379","DOI":"10.1145\/2732587.2732597"},{"key":"21359_CR3","doi-asserted-by":"crossref","unstructured":"Xie M, Jean N, Burke M, Lobell D, and Stefano Ermon (2016). Transfer learning from deep features for remote sensingpoverty mapping. In Thirtieth AAAI Conference on Artificial Intelligence","DOI":"10.1609\/aaai.v30i1.9906"},{"key":"21359_CR4","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"issue":"6301","key":"21359_CR5","doi-asserted-by":"publisher","first-page":"790","DOI":"10.1126\/science.aaf7894","volume":"353","author":"Neal Jean","year":"2016","unstructured":"Jean Neal, Burke Marshall, Xie Michael, Davis W. Matthew, Lobell David B., Ermon Stefano (2016) Combining satellite imagery and machine learning to predict poverty. Science 353(6301):790\u2013794","journal-title":"Science"},{"key":"21359_CR6","unstructured":"Duan L, Cheng THE, Zhu J, Gao C (2017) Deep convolutional neural networks for spatiotemporal crime prediction. In Proceedings of the International Conference on Information and Knowledge Engineering (IKE), pp. 61\u201367. The Steering Committee of the World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp)"},{"issue":"127","key":"21359_CR7","doi-asserted-by":"publisher","DOI":"10.1098\/rsif.2016.0690","volume":"14","author":"JessicaE. Steele","year":"2017","unstructured":"Steele Jessica E., Sunds\u00f8y P\u00e5l Roe, Pezzulo Carla, Alegana Victor A., Bird Tomas J., Blumenstock Joshua, Bjelland Johannes et al (2017) Mapping poverty using mobile phone and satellite data. J R Soc Interface 14(127):20160690","journal-title":"J R Soc Interface"},{"key":"21359_CR8","unstructured":"Suraj PK, Gupta A, Sharma M, Paul SB, Banerjee S (2017) On monitoring development indicators using high resolution satellite images. arXiv preprint arXiv:1712.02282.\u00a0https:\/\/arxiv.org\/abs\/1712.02282"},{"issue":"2","key":"21359_CR9","doi-asserted-by":"publisher","first-page":"231","DOI":"10.5958\/0974-0279.2018.00040.X","volume":"31","author":"SP Subash","year":"2018","unstructured":"Subash SP, Kumar RR (2018) Aditya. Satellite data and machine learning tools for predicting poverty in rural India. Agric Econ Res Rev 31(2):231\u2013240","journal-title":"Agric Econ Res Rev"},{"issue":"9","key":"21359_CR10","doi-asserted-by":"publisher","first-page":"2690","DOI":"10.1080\/01431161.2017.1420936","volume":"39","author":"E Dugoua","year":"2018","unstructured":"Dugoua E, Kennedy R, Urpelainen J (2018) Satellite data for the social sciences: measuring rural electrification with night-time lights. Int J Remote Sens 39(9):2690\u20132701","journal-title":"Int J Remote Sens"},{"key":"21359_CR11","doi-asserted-by":"crossref","unstructured":"Pandey SM, Agarwal T, Narayanan C (2018) Krishnan. Multi-task deep learning for predicting poverty from satellite images. In Thirty-Second AAAI Conference on Artificial Intelligence","DOI":"10.1609\/aaai.v32i1.11416"},{"issue":"11","key":"21359_CR12","doi-asserted-by":"publisher","DOI":"10.3390\/rs11111282","volume":"11","author":"Alireza Ajami","year":"2019","unstructured":"Ajami Alireza, Kuffer Monika, Persello Claudio, Pfeffer Karin (2019) Identifying a slums\u2019 degree of deprivation from VHR images using convolutional neural networks. Remote Sens 11(11):1282","journal-title":"Remote Sens"},{"key":"21359_CR13","doi-asserted-by":"crossref","unstructured":"Sheehan E, Meng C, Tan M, Uzkent B, Jean N, Burke M, Lobell D, and Stefano Ermon (2019). Predicting economic development using geolocated wikipedia articles. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2698\u20132706","DOI":"10.1145\/3292500.3330784"},{"issue":"15","key":"21359_CR14","doi-asserted-by":"publisher","first-page":"5716","DOI":"10.1080\/01431161.2019.1580820","volume":"40","author":"G Li","year":"2019","unstructured":"Li G, Cai Z, Liu X, Liu J, Su S (2019) A comparison of machine learning approaches for identifying high-poverty counties: robust features of DMSP\/OLS night-time light imagery. Int J Remote Sens 40(15):5716\u20135736","journal-title":"Int J Remote Sens"},{"key":"21359_CR15","unstructured":"Rastogi R Temporal Poverty Prediction In Developing Countries. https:\/\/cs229.stanford.edu\/proj2017\/final-reports\/5244050.pdf"},{"issue":"4","key":"21359_CR16","doi-asserted-by":"publisher","first-page":"1213","DOI":"10.1073\/pnas.1812969116","volume":"116","author":"GaryR. Watmough","year":"2019","unstructured":"Watmough Gary R., Marcinko Charlotte LJ, Sullivan Clare, Tschirhart Kevin, Mutuo Patrick K., Palm Cheryl A., Svenning Jens-Christian (2019) Socioecologically informed use of remote sensing data to predict rural household poverty. Proc Natl Acad Sci U S A 116(4):1213\u20131218","journal-title":"Proc Natl Acad Sci U S A"},{"key":"21359_CR17","unstructured":"Perez A, Ganguli S, Ermon S, Azzari G, Burke M, Lobell D (2019) Semi-supervised multitask learning on multispectral satellite images using wasserstein generative adversarial networks (gans) for predicting poverty. arXiv preprint arXiv: 1902.11110"},{"key":"21359_CR18","unstructured":"Piaggesi S, Gauvin L, Tizzoni M, Cattuto C, Adler N, Verhulst S, Young A, Price R, Ferres L, and Andre Panisson (2019). Predicting City Poverty Using Satellite Imagery. In Proceedings of the IEEE Conference on Computer VisionPattern Recognition Workshops, pp. 90\u201396"},{"key":"21359_CR19","doi-asserted-by":"crossref","unstructured":"Cadamuro G, Muhebwa A (2019) and Jay Taneja. Street smarts: measuring intercity road quality using deep learning on satellite imagery. In Proceedings of the 2nd ACM SIGCAS Conference on Computing and Sustainable Societies, pp. 145\u2013154","DOI":"10.1145\/3314344.3332493"},{"key":"21359_CR20","doi-asserted-by":"crossref","unstructured":"Tingzon I, Orden A, Sy S, Sekara V, Weber I, Fatehkia M (2019) Manuel Garcia Herranz, and Dohyung Kim. Mapping poverty in the Philippines using machine learning, satellite imagery, and crowd-sourced geospatial information. In AI for Social Good ICML 2019 Workshop","DOI":"10.5194\/isprs-archives-XLII-4-W19-425-2019"},{"issue":"3","key":"21359_CR21","doi-asserted-by":"publisher","first-page":"164","DOI":"10.35940\/ijrte.C3918.098319","volume":"8","author":"Parth Agarwal","year":"2019","unstructured":"Agarwal Parth, Garg Nandishwar, Singh Pratibha (2019) Predicting poverty index using deep learning on remote sensing and household data. International Journal of Recent Technology and Engineering (IJRTE) 8(3):164\u2013168","journal-title":"International Journal of Recent Technology and Engineering (IJRTE)"},{"key":"21359_CR22","doi-asserted-by":"crossref","unstructured":"Bansal C, Jain A, Barwaria P, Choudhary A, Singh A, Gupta A, Seth A (2020) Temporal Prediction of Socio-Economic Indicators Using Satellite Imagery. In Proceedings of the 7th ACM IKDD CoDS and 25th COMAD, pp. 73\u201381","DOI":"10.1145\/3371158.3371167"},{"key":"21359_CR23","unstructured":"Shekhar S, Singh P, Tiwari S (2021) Predicting poverty index on satellite images and DHS data using transfer learning. Natural Volatiles & Essential Oils 8(5):2184\u20132192.\u00a0https:\/\/www.nveo.org\/index.php\/journal\/article\/view\/768\/698"},{"issue":"3","key":"21359_CR24","doi-asserted-by":"publisher","DOI":"10.3390\/su13031412","volume":"13","author":"Adham Alsharkawi","year":"2021","unstructured":"Alsharkawi Adham, Al-Fetyani Mohammad, Dawas Maha, Saadeh Heba, Alyaman Musa (2021) Poverty classification using machine learning: the case of Jordan. Sustainability 13(3):1412","journal-title":"Sustainability"},{"key":"21359_CR25","doi-asserted-by":"crossref","unstructured":"Ayush K, Uzkent B (2021) Kumar Tanmay3 Marshall Burke2 David Lobell, and Stefano Ermon. Efficient Poverty Mapping from High Resolution Remote Sensing Images. In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 1, pp. 12\u201320","DOI":"10.1609\/aaai.v35i1.16072"},{"key":"21359_CR26","unstructured":"Ridge, Regression L https:\/\/towardsdatascience.com\/ridge-and-lasso-regression-a-complete-guide-with-python-scikit-learn-e20e34bcbf0b"},{"key":"21359_CR27","unstructured":"Nightlight Images DMSP-OLS, Dataset https:\/\/ngdc.noaa.gov\/eog\/download.html"},{"key":"21359_CR28","unstructured":"DHS program https:\/\/dhsprogram.com\/data\/available-datasets.cfm"},{"key":"21359_CR29","unstructured":"Daylight, Images Google HERE Maps API, www.here.com"},{"key":"21359_CR30","unstructured":"Regression R https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.linear_model.Ridge.html"},{"key":"21359_CR31","unstructured":"Regression L https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.linear_model.Lasso.html"},{"key":"21359_CR32","unstructured":"Regression R https:\/\/scikit-learn.org\/stable\/auto_examples\/linear_model\/plot_ransac.html#"},{"key":"21359_CR33","unstructured":"Regression KNN\u00a0https:\/\/scikit-learn.org\/stable\/auto_examples\/neighbors\/plot_regression.html#sphx-glr-auto-examples-neighbors-plot-regression-py"},{"key":"21359_CR34","unstructured":"Pearson\u2019s Correlation, Coefficient https:\/\/en.wikipedia.org\/wiki\/Pearson_correlation_coefficient"},{"issue":"10","key":"21359_CR35","doi-asserted-by":"publisher","first-page":"5004","DOI":"10.1109\/JBHI.2022.3223127","volume":"27","author":"M Kaur","year":"2023","unstructured":"Kaur M, Singh D, Kumar V, Lee H-N (2023) Mlnet: metaheuristics-based lightweight deep learning network for cervical cancer diagnosis. IEEE J Biomed Health Inform 27(10):5004\u20135014. https:\/\/doi.org\/10.1109\/JBHI.2022.3223127","journal-title":"IEEE J Biomed Health Inform"},{"key":"21359_CR36","unstructured":"Tiwari S, Singh P, Shekhar S Predicting Wealthscore from Remote Sensing Satellite Images and Household Survey Data using Deep Transfer Learning, submitted"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-026-21359-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-026-21359-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-026-21359-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T11:02:45Z","timestamp":1778151765000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-026-21359-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,7]]},"references-count":36,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["21359"],"URL":"https:\/\/doi.org\/10.1007\/s11042-026-21359-7","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,7]]},"assertion":[{"value":"15 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 March 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 February 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 May 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Authors have the authorization to use the survey data on this research work. We have extended our work on the same data for solving the poverty prediction problem [\n                      \n                      ].","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and informed consent for data used"}},{"value":"The authors declare that there is no conflict of interest regarding the publication of this paper.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The authors have not received any financial support from any organization for this submitted research work.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"503"}}