SATELLITE AND ARTIFICIAL INTELLIGENCE IN MAPPING MULTIDIMENSIONAL POVERTY IN AFRICA
DOI:
https://doi.org/10.48346/IMIST.PRSM/ajlp-gs.v7i1.44087Keywords:
Africa, Artificial Intelligence (AI), Geospatial, Mapping, Multidimensional Poverty Index (MPI), SatelliteAbstract
Context and background
Multidimensional Poverty (MP) considers poverty in multiple dimensions of deprivations such as health, education, energy, the standard of living and access to basic services. MP remains a major challenge in Africa, with a large proportion of the population living in MP. According to United Nations Development Programme (UNDP), Africa has shown the highest Multidimensional Poverty Index (MPI) having over 40% of its population living in MP.
Goal and Objectives:
This paper is a review, aimed at assessing the potential of the integration of satellite and Artificial Intelligence (AI) in mapping MP, with a specific focus on Africa.
Methodology:
Based on the reviews of past studies, the combination of satellite data such as nighttime light, daytime satellite imagery and high-resolution settlement data in combination with techniques such as field surveys, statistical correlation models (transfer learning) and AI (deep learning) has been applied in mapping MP.
Results:
The findings from studies show that the combination of satellite data and AI has the capability of providing more accurate and granular MP maps, compared to the traditional approach. Again, this paper explains the concept of MP with a specific focus on Africa and presents a map depicting the current MPI in African countries. Finally, pitfalls especially in the accuracy, granularity and frequency of MP data were identified. Consequently, the satellite and AI approaches are recommended for more accurate, frequent, cost-effective and granular data, required in mapping poverty and design of interventions that effectively address the needs of the vulnerable populations in Africa.
References
Abdel-Rahman, E. M., Ahmed, F. B., & Ismail, R. (2013). Random forest regression and spectral band selection for estimating sugarcane leaf nitrogen concentration using EO-1 Hyperion hyperspectral data. International Journal of Remote Sensing, 34(2), 712–728. https://doi.org/10.1080/01431161.2012.713142
Abeje, M. T., Tsunekawa, A., Haregeweyn, N., Ayalew, Z., Nigussie, Z., Berihun, D., Adgo, E., & Elias, A. (2020). Multidimensional Poverty and Inequality: Insights from the Upper Blue Nile Basin, Ethiopia. Social Indicators Research, 149(2), 585–611. https://doi.org/10.1007/s11205-019-02257-y
Achdut, N., Refaeli, T., & Schwartz Tayri, T. M. (2021). Subjective Poverty, Material Deprivation Indices and Psychological Distress Among Young Adults: The Mediating Role of Social Capital and Usage of Online Social Networks. Social Indicators Research, 158(3), 863–887. https://doi.org/10.1007/s11205-021-02729-0
Alkire, S., & Foster, J. E. (2011). Counting and Multidimensional Poverty Measurement . Journal of Public Economics, 95(7–8).
Asian Development Bank (ADB). (2020). Mapping Poverty through Data Integration and Artificial Intelligence: https://doi.org/10.22617/FLS200215-3
Ayush, K., Uzkent, B., Burke, M., Lobell, D., & Ermon, S. (2020). Generating interpretable poverty maps using object detection in satellite images. Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20) Special Track on AI for Computational Sustainability and Human Well-Being, 4410–4416.
Ayush, K., Uzkent, B., Tanmay, K., Burke, M., Lobell, D., & Ermon, S. (2021). Efficient Poverty Mapping from High Resolution Remote Sensing Images. Proceedings of the AAAI Conference on Artificial Intelligence, 35(1), 12–20. https://doi.org/10.1609/aaai.v35i1.16072
Batana, Y. M. (2013). Multidimensional Measurement of Poverty Among Women in Sub-Saharan Africa. Social Indicators Research, 112(2), 337–362. https://doi.org/10.1007/s11205-013-0251-9
Belgiu, M., & Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011
Berenger, V. (2019). The counting approach to multidimensional poverty. The case of four African countries. South African Journal of Economics, 87(2), 200–227. https://doi.org/10.1111/saje.12217
Bigman, D., & Fofack, H. (2000). Geographical Targeting for Poverty Alleviation. The World Bank. https://doi.org/10.1596/0-8213-4625-3
Blumenstock, J., Cadamuro, G., & On, R. (2015). Predicting poverty and wealth from mobile phone metadata. Science, 350(6264), 1073–1076. https://doi.org/10.1126/science.aac4420
Cadamuro, G., Muhebwa, A., & Taneja, J. (2018). Assigning a Grade: Accurate Measurement of Road Quality Using Satellite Imagery.
Chand, T. R. K., Badarinath, K. V. S., Elvidge, C. D., & Tuttle, B. T. (2009). Spatial characterization of electrical power consumption patterns over India using temporal DMSP‐OLS night‐time satellite data. International Journal of Remote Sensing, 30(3), 647–661. https://doi.org/10.1080/01431160802345685
Chen, X., & Nordhaus, W. D. (2011). Using luminosity data as a proxy for economic statistics. Proceedings of the National Academy of Sciences, 108(21), 8589–8594. https://doi.org/10.1073/pnas.1017031108
Chen, Z., Yu, B., Hu, Y., Huang, C., Shi, K., & Wu, J. (2015). Estimating House Vacancy Rate in Metropolitan Areas Using NPP-VIIRS Nighttime Light Composite Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(5), 2188–2197. https://doi.org/10.1109/JSTARS.2015.2418201
Chen, Z., Yu, B., Song, W., Liu, H., Wu, Q., Shi, K., & Wu, J. (2017). A New Approach for Detecting Urban Centers and Their Spatial Structure With Nighttime Light Remote Sensing. IEEE Transactions on Geoscience and Remote Sensing, 55(11), 6305–6319. https://doi.org/10.1109/TGRS.2017.2725917
David J. Rogers, Thomas Emwanu, & Timothy P. Robinson. (2006). Poverty Mapping in Uganda: An Analysis Using Remotely Sensed and Other Environmental Data. Rogers2006PovertyMI.
Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., Martimort, P., Meygret, A., Spoto, F., Sy, O., Marchese, F., & Bargellini, P. (2012). Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services. Remote Sensing of Environment, 120, 25–36. https://doi.org/10.1016/j.rse.2011.11.026
Duque, J. C., Patino, J. E., Ruiz, L. A., & Pardo-Pascual, J. E. (2015). Measuring intra-urban poverty using land cover and texture metrics derived from remote sensing data. Landscape and Urban Planning, 135, 11–21. https://doi.org/10.1016/j.landurbplan.2014.11.009
Ehrlich D., Schiavina M., Pesaresi M., & Kemper, T. (2018). Detecting spatial pattern of inequalities from remote sensing Towards mapping of deprived communities and poverty.
Elvidge, C. D., Baugh, K. E., Zhizhin, M., & Hsu, F.-C. (2013). Why VIIRS data are superior to DMSP for mapping nighttime lights. Proceedings of the Asia-Pacific Advanced Network, 35(0), 62. https://doi.org/10.7125/APAN.35.7
Elvidge, C. D., Sutton, P. C., Ghosh, T., Tuttle, B. T., Baugh, K. E., Bhaduri, B., & Bright, E. (2009). A global poverty map derived from satellite data. Computers & Geosciences, 35(8), 1652–1660. https://doi.org/10.1016/j.cageo.2009.01.009
Fisher, J. R. B., Acosta, E. A., Dennedy-Frank, P. J., Kroeger, T., & Boucher, T. M. (2018). Impact of satellite imagery spatial resolution on land use classification accuracy and modeled water quality. Remote Sensing in Ecology and Conservation, 4(2), 137–149. https://doi.org/10.1002/rse2.61
Foster, J. E. (1998). Absolute versus Relative Poverty. The American Economic Review, 88, 335–341.
Frediani, A. A. (2010). Sen’s Capability Approach as a framework to the practice of development. Development in Practice, 20(2), 173–187. https://doi.org/10.1080/09614520903564181
Gambo, J., Shafri, H. Z. M., & Yusuf, Y. A. (2022). An analysis of multidimensional poverty in Nigeria using statistical and geospatial modelling: A case study of Jigawa state. IOP Conference Series: Earth and Environmental Science, 1064(1), 012047. https://doi.org/10.1088/1755-1315/1064/1/012047
Gervasoni, L., Fenet, S., Perrier, R., & Sturm, P. (2018). Convolutional neural networks for disaggregated population mapping using open data. 5th IEEE International Conference on Data Science and Advanced Analytics (DSAA), IEEE, , 594–603.
Ghosh, T., Anderson, S., Elvidge, C., & Sutton, P. (2013). Using Nighttime Satellite Imagery as a Proxy Measure of Human Well-Being. Sustainability, 5(12), 4988–5019. https://doi.org/10.3390/su5124988
Gong, P., Wang, J., Yu, L., Zhao, Y., Zhao, Y., Liang, L., Niu, Z., Huang, X., Fu, H., Liu, S., Li, C., Li, X., Fu, W., Liu, C., Xu, Y., Wang, X., Cheng, Q., Hu, L., Yao, W., … Chen, J. (2013). Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data. International Journal of Remote Sensing, 34(7), 2607–2654. https://doi.org/10.1080/01431161.2012.748992
Grippa, T., Georganos, S., Zarougui, S., Bognounou, P., Diboulo, E., Forget, Y., Lennert, M., Vanhuysse, S., Mboga, N., & Wolff, E. (2018). Mapping Urban Land Use at Street Block Level Using OpenStreetMap, Remote Sensing Data, and Spatial Metrics. ISPRS International Journal of Geo-Information, 7(7), 246. https://doi.org/10.3390/ijgi7070246
Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T. R., Kommareddy, A., Egorov, A., Chini, L., Justice, C. O., & Townshend, J. R. G. (2013). High-Resolution Global Maps of 21st-Century Forest Cover Change. Science, 342(6160), 850–853. https://doi.org/10.1126/science.1244693
Head, A., Manguin, M., Tran, N., & Blumenstock, J. E. (2017). Can Human Development be Measured with Satellite Imagery? Proceedings of the Ninth International Conference on Information and Communication Technologies and Development, 1–11. https://doi.org/10.1145/3136560.3136576
Henderson, J. V., Storeygard, A., & Weil, D. N. (2012). Measuring Economic Growth from Outer Space. American Economic Review, 102(2), 994–1028. https://doi.org/10.1257/aer.102.2.994
Herold, M., Liu, X., & Clarke, K. C. (2003). Spatial Metrics and Image Texture for Mapping Urban Land Use. Photogrammetric Engineering & Remote Sensing, 69(9), 991–1001. https://doi.org/10.14358/PERS.69.9.991
Herold, M., Scepan, J., & Clarke, K. C. (2002). The Use of Remote Sensing and Landscape Metrics to Describe Structures and Changes in Urban Land Uses. Environment and Planning A: Economy and Space, 34(8), 1443–1458. https://doi.org/10.1068/a3496
Hersh, J., Engstrom, R., & Mann, M. (2021). Open data for algorithms: mapping poverty in Belize using open satellite derived features and machine learning. Information Technology for Development, 27(2), 263–292. https://doi.org/10.1080/02681102.2020.1811945
Hofer, M., Sako, T., Martinez Jr., A., Addawe, M., Bulan, J., Durante, R. L., & Martillan, M. (2020). Applying Artificial Intelligence on Satellite Imagery to Compile Granular Poverty Statistics. https://doi.org/10.22617/WPS200432-2
Immitzer, M., Atzberger, C., & Koukal, T. (2012). Tree Species Classification with Random Forest Using Very High Spatial Resolution 8-Band WorldView-2 Satellite Data. Remote Sensing, 4(9), 2661–2693. https://doi.org/10.3390/rs4092661
Imran, M., Stein, A., & Zurita-Milla, R. (2014). Investigating rural poverty and marginality in Burkina Faso using remote sensing-based products. International Journal of Applied Earth Observation and Geoinformation, 26, 322–334. https://doi.org/10.1016/j.jag.2013.08.012
Jarry, R., Chaumont, M., Berti-Équille, L., & Subsol, G. (2021). Assessment of CNN-Based Methods for Poverty Estimation from Satellite Images (pp. 550–565). https://doi.org/10.1007/978-3-030-68787-8_40
Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D. B., & Ermon, S. (2016). Combining satellite imagery and machine learning to predict poverty. Science, 353(6301), 790–794. https://doi.org/10.1126/science.aaf7894
Jemmali, H. (2017). Mapping water poverty in Africa using the improved Multidimensional Index of Water Poverty. International Journal of Water Resources Development, 33(4), 649–666. https://doi.org/10.1080/07900627.2016.1219941
Jolliffe, D. M., Mahler, D. G., Lakner, C., Atamanov, A., & Tetteh Baah, S. Kofi. (2017). Assessing the Impact of the 2017 PPPs on the International Poverty Line and Global Poverty (WPS 9941). http://documents.worldbank.org/curated/en/353811645450974574/Assessing-the-Impactof-the-2017-PPPs-on-the-International-Poverty-Line-and-Global-Poverty
Klemens, B., Coppola, A., & Shron, M. (2015). Estimating Local Poverty Measures Using Satellite Images: A Pilot Application to Central America. The World Bank. https://doi.org/10.1596/1813-9450-7329
Kohli, D., Sliuzas, R., & Stein, A. (2016). Urban slum detection using texture and spatial metrics derived from satellite imagery. Journal of Spatial Science, 61(2), 405–426. https://doi.org/10.1080/14498596.2016.1138247
Kohli, D., Stein, A., Sliuzas, R., & Kerle, N. (2015). Identifying and Classifying Slum Areas Using Remote Sensing.
Kohli, D., Warwadekar, P., Kerle, N., Sliuzas, R., & Stein, A. (2013). Transferability of Object-Oriented Image Analysis Methods for Slum Identification. Remote Sensing, 5(9), 4209–4228. https://doi.org/10.3390/rs5094209
Lin, L., Di, L., Tang, J., Yu, E., Zhang, C., Rahman, Md., Shrestha, R., & Kang, L. (2019). Improvement and Validation of NASA/MODIS NRT Global Flood Mapping. Remote Sensing, 11(2), 205. https://doi.org/10.3390/rs11020205
Lin, L., Di, L., Yang, R., Zhang, C., Yu, E., Rahman, M. S., Sun, Z., & Tang, J. (2018). Using Machine Learning Approach to Evaluate the PM2.5 Concentrations in China from 1998 to 2016. Proceedings of the 2018 7th International Conference on Agro-Geoinformatics (Agro-Geoinformatics), 1–5.
Lin, L., Di, L., Zhang, C., Guo, L., & Di, Y. (2021). Remote Sensing of Urban Poverty and Gentrification. Remote Sensing, 13(20), 4022. https://doi.org/10.3390/rs13204022
Ma, T., Zhou, C., Pei, T., Haynie, S., & Fan, J. (2014). Responses of Suomi-NPP VIIRS-derived nighttime lights to socioeconomic activity in China’s cities. Remote Sensing Letters, 5(2), 165–174. https://doi.org/10.1080/2150704X.2014.890758
Mahabir, R., Croitoru, A., Crooks, A., Agouris, P., & Stefanidis, A. (2018). A Critical Review of High and Very High-Resolution Remote Sensing Approaches for Detecting and Mapping Slums: Trends, Challenges and Emerging Opportunities. Urban Science, 2(1), 8. https://doi.org/10.3390/urbansci2010008
Marx, S., Phalkey, R., Aranda-Jan, C. B., Profe, J., Sauerborn, R., & Höfle, B. (2014). Geographic information analysis and web-based geoportals to explore malnutrition in Sub-Saharan Africa: a systematic review of approaches. BMC Public Health, 14(1), 1189. https://doi.org/10.1186/1471-2458-14-1189
Melchiorri, M., Florczyk, A., Freire, S., Schiavina, M., Pesaresi, M., & Kemper, T. (2018). Unveiling 25 Years of Planetary Urbanization with Remote Sensing: Perspectives from the Global Human Settlement Layer. Remote Sensing, 10(5), 768. https://doi.org/10.3390/rs10050768
Noor, A. M., Alegana, V. A., Gething, P. W., Tatem, A. J., & Snow, R. W. (2008). Using remotely sensed night-time light as a proxy for poverty in Africa. Population Health Metrics, 6(1), 5. https://doi.org/10.1186/1478-7954-6-5
Pekel, J.-F., Cottam, A., Gorelick, N., & Belward, A. S. (2016). High-resolution mapping of global surface water and its long-term changes. Nature, 540(7633), 418–422. https://doi.org/10.1038/nature20584
Pesaresi, M., Syrris, V., & Julea, A. (2016). A New Method for Earth Observation Data Analytics Based on Symbolic Machine Learning. Remote Sensing, 8(5), 399. https://doi.org/10.3390/rs8050399
Piaggesi, S., Gauvin, L., Tizzoni, M., Cattuto, C., Adler, N., Verhulst, S. G., Young, A., Price, R., Ferres, L., & Panisson, A. (2019). Predicting City Poverty Using Satellite Imagery. . Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 90–96.
Priambodo, A. (2021). THE IMPACT OF UNEMPLOYMENT AND POVERTY ON ECONOMIC GROWTH AND THE HUMAN DEVELOPMENT INDEX (HDI). Perwira International Journal of Economics & Business, 1(1), 29–36. https://doi.org/10.54199/pijeb.v1i1.43
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., & Fei-Fei, L. (2015). ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision, 115(3), 211–252. https://doi.org/10.1007/s11263-015-0816-y
Sheehan, E., Meng, C., Tan, M., Uzkent, B., Jean, N., Burke, M., Lobell, D., & Ermon, S. (2019). Predicting economic development using geolocated wikipedia articles. , 2698–2706. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2698–2706.
Shi, K., Yang, Q., Fang, G., Yu, B., Chen, Z., Yang, C., & Wu, J. (2019). Evaluating spatiotemporal patterns of urban electricity consumption within different spatial boundaries: A case study of Chongqing, China. Energy, 167, 641–653. https://doi.org/10.1016/j.energy.2018.11.022
Sohnesen, T. P., Fisker, P., & Malmgren‐Hansen, D. (2022). Using Satellite Data to Guide Urban Poverty Reduction. Review of Income and Wealth, 68(S2). https://doi.org/10.1111/roiw.12552
Steele, J. E., Sundsøy, P. R., Pezzulo, C., Alegana, V. A., Bird, T. J., Blumenstock, J., Bjelland, J., Engø-Monsen, K., de Montjoye, Y.-A., Iqbal, A. M., Hadiuzzaman, K. N., Lu, X., Wetter, E., Tatem, A. J., & Bengtsson, L. (2017). Mapping poverty using mobile phone and satellite data. Journal of The Royal Society Interface, 14(127), 20160690. https://doi.org/10.1098/rsif.2016.0690
Stevens, F. R., Gaughan, A. E., Linard, C., & Tatem, A. J. (2015). Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLOS ONE, 10(2), e0107042. https://doi.org/10.1371/journal.pone.0107042
Stoler, J., Daniels, D., Weeks, J. R., Stow, D. A., Coulter, L. L., & Finch, B. K. (2012). Assessing the Utility of Satellite Imagery with Differing Spatial Resolutions for Deriving Proxy Measures of Slum Presence in Accra, Ghana. GIScience & Remote Sensing, 49(1), 31–52. https://doi.org/10.2747/1548-1603.49.1.31
Stow, D. A., Lippitt, C. D., & Weeks, J. R. (2010). Geographic Object-based Delineation of Neighborhoods of Accra, Ghana Using QuickBird Satellite Imagery. Photogrammetric Engineering & Remote Sensing, 76(8), 907–914. https://doi.org/10.14358/PERS.76.8.907
Stow, D., Lopez, A., Lippitt, C., Hinton, S., & Weeks, J. (2007). Object‐based classification of residential land use within Accra, Ghana based on QuickBird satellite data. International Journal of Remote Sensing, 28(22), 5167–5173. https://doi.org/10.1080/01431160701604703
Tingzon, I., Orden, A., Go, K. T., Sy, S., Sekara, V., Weber, I., Fatehkia, M., García-Herranz, M., & Kim, D. (2019). MAPPING POVERTY IN THE PHILIPPINES USING MACHINE LEARNING, SATELLITE IMAGERY, AND CROWD-SOURCED GEOSPATIAL INFORMATION. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-4/W19, 425–431. https://doi.org/10.5194/isprs-archives-XLII-4-W19-425-2019
United Nations General Assembly. (2015). Transforming Our World: The 2030 Agenda for Sustainable Development.
van Beijma, S., Comber, A., & Lamb, A. (2014). Random forest classification of salt marsh vegetation habitats using quad-polarimetric airborne SAR, elevation and optical RS data. Remote Sensing of Environment, 149, 118–129. https://doi.org/10.1016/j.rse.2014.04.010
Varshney, K. R., Chen, G. H., Abelson, B., Nowocin, K., Sakhrani, V., Xu, L., & Spatocco, B. L. (2015). Targeting Villages for Rural Development Using Satellite Image Analysis. Big Data, 3(1), 41–53. https://doi.org/10.1089/big.2014.0061
Victor, B., Blevins, M., Green, A. F., Ndatimana, E., González-Calvo, L., Fischer, E. F., Vergara, A. E., Vermund, S. H., Olupona, O., & Moon, T. D. (2014). Multidimensional Poverty in Rural Mozambique: A New Metric for Evaluating Public Health Interventions. PLoS ONE, 9(9), e108654. https://doi.org/10.1371/journal.pone.0108654
Weeks, J. R., Hill, A., Stow, D., Getis, A., & Fugate, D. (2007). Can we spot a neighborhood from the air? Defining neighborhood structure in Accra, Ghana. GeoJournal, 69(1–2), 9–22. https://doi.org/10.1007/s10708-007-9098-4
World Bank. (2018). Poverty and Shared Prosperity 2018: Piecing Together the Poverty Puzzle. https://www.worldbank.org/en/publication/poverty-and-shared-prosperity-2018
World Bank. (2020). Poverty and Shared Prosperity 2020: Reversals of Fortune. . https://openknowledge.worldbank.org/handle/10986/34496
World Bank. (2022). Poverty and Shared Prosperity 2022: Correcting Course. https://www.worldbank.org/en/publication/poverty-and-shared-prosperity
Xie, M., Jean, N., Burke, M., Lobell, D., & Ermon, S. (2016). Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.9906
Yao, Y., Liu, X., Li, X., Zhang, J., Liang, Z., Mai, K., & Zhang, Y. (2017). Mapping fine-scale population distributions at the building level by integrating multisource geospatial big data. International Journal of Geographical Information Science, 1–25. https://doi.org/10.1080/13658816.2017.1290252
Yeh, C., Perez, A., Driscoll, A., Azzari, G., Tang, Z., Lobell, D., Ermon, S., & Burke, M. (2020). Using publicly available satellite imagery and deep learning to understand economic well-being in Africa. Nature Communications, 11(1), 2583. https://doi.org/10.1038/s41467-020-16185-w
Yu, B., Shi, K., Hu, Y., Huang, C., Chen, Z., & Wu, J. (2015). Poverty Evaluation Using NPP-VIIRS Nighttime Light Composite Data at the County Level in China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(3), 1217–1229. https://doi.org/10.1109/JSTARS.2015.2399416
Yu, B., Tang, M., Wu, Q., Yang, C., Deng, S., Shi, K., Peng, C., Wu, J., & Chen, Z. (2018). Urban Built-Up Area Extraction From Log- Transformed NPP-VIIRS Nighttime Light Composite Data. IEEE Geoscience and Remote Sensing Letters, 15(8), 1279–1283. https://doi.org/10.1109/LGRS.2018.2830797
Zhang, C., Yang, Z., Di, L., Lin, L., & Hao, P. (2020). REFINEMENT OF CROPLAND DATA LAYER USING MACHINE LEARNING. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-3/W11, 161–164. https://doi.org/10.5194/isprs-archives-XLII-3-W11-161-2020
Zhao, N., Liu, Y., Cao, G., Samson, E. L., & Zhang, J. (2017). Forecasting China’s GDP at the pixel level using nighttime lights time series and population images. GIScience & Remote Sensing, 54(3), 407–425. https://doi.org/10.1080/15481603.2016.1276705
Zhao, X., Yu, B., Liu, Y., Chen, Z., Li, Q., Wang, C., & Wu, J. (2019). Estimation of Poverty Using Random Forest Regression with Multi-Source Data: A Case Study in Bangladesh. Remote Sensing, 11(4), 375. https://doi.org/10.3390/rs11040375
Zhou, Y., Ma, T., Zhou, C., & Xu, T. (2015). Nighttime Light Derived Assessment of Regional Inequality of Socioeconomic Development in China. Remote Sensing, 7(2), 1242–1262. https://doi.org/10.3390/rs70201242
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 African Journal on Land Policy and Geospatial Sciences
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The African Journal on Land Policy and Geospatial Sciences (AJLP&GS) is made available under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License https://creativecommons.org/licenses/by-nc-sa/4.0/. Authors with free access retain the copyright of their manuscripts. All open access manuscripts are published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided that the original work is properly cited. The use of general descriptive names, trade names, trademarks, etc. in this publication, even if not specifically identified, does not imply that applicable laws and regulations do not protect these names. Even though the advice and information in this journal are true and accurate as of the date of its publication, neither the authors, the editor, nor the publisher can assume any legal responsibility for any errors or omissions.
