Aplicación de una red neuronal convolucional para la predicción de mallas de población en el área metropolitana de Bogotá

ilustraciones, fotografías, mapas, tablas

Autores:: Montero Leguizamón, Aníbal

Tipo de recurso:

Fecha de publicación:: 2021

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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repository_id_str
dc.title.spa.fl_str_mv	Aplicación de una red neuronal convolucional para la predicción de mallas de población en el área metropolitana de Bogotá
dc.title.translated.eng.fl_str_mv	Predicting a population grid in the Bogotá metropolitan area, based on convolutional neural networks
title	Aplicación de una red neuronal convolucional para la predicción de mallas de población en el área metropolitana de Bogotá
spellingShingle	Aplicación de una red neuronal convolucional para la predicción de mallas de población en el área metropolitana de Bogotá 000 - Ciencias de la computación, información y obras generales Redes neuronales Neural networks Malla de Población Sensores Remotos Aprendizaje Profundo Redes Neuronales Convolucionales Population grid Remote Sensing Deep Learning Convolutional Neural Networks Proyección demográfica Population projections
title_short	Aplicación de una red neuronal convolucional para la predicción de mallas de población en el área metropolitana de Bogotá
title_full	Aplicación de una red neuronal convolucional para la predicción de mallas de población en el área metropolitana de Bogotá
title_fullStr	Aplicación de una red neuronal convolucional para la predicción de mallas de población en el área metropolitana de Bogotá
title_full_unstemmed	Aplicación de una red neuronal convolucional para la predicción de mallas de población en el área metropolitana de Bogotá
title_sort	Aplicación de una red neuronal convolucional para la predicción de mallas de población en el área metropolitana de Bogotá
dc.creator.fl_str_mv	Montero Leguizamón, Aníbal
dc.contributor.advisor.none.fl_str_mv	Niño Vásquez, Luís Fernando
dc.contributor.author.none.fl_str_mv	Montero Leguizamón, Aníbal
dc.contributor.researchgroup.spa.fl_str_mv	LABORATORIO DE INVESTIGACIÓN EN SISTEMAS INTELIGENTES - LISI
dc.subject.ddc.spa.fl_str_mv	000 - Ciencias de la computación, información y obras generales
topic	000 - Ciencias de la computación, información y obras generales Redes neuronales Neural networks Malla de Población Sensores Remotos Aprendizaje Profundo Redes Neuronales Convolucionales Population grid Remote Sensing Deep Learning Convolutional Neural Networks Proyección demográfica Population projections
dc.subject.lemb.spa.fl_str_mv	Redes neuronales
dc.subject.lemb.eng.fl_str_mv	Neural networks
dc.subject.proposal.spa.fl_str_mv	Malla de Población Sensores Remotos Aprendizaje Profundo Redes Neuronales Convolucionales
dc.subject.proposal.eng.fl_str_mv	Population grid Remote Sensing Deep Learning Convolutional Neural Networks
dc.subject.unesco.spa.fl_str_mv	Proyección demográfica
dc.subject.unesco.eng.fl_str_mv	Population projections
description	ilustraciones, fotografías, mapas, tablas
publishDate	2021
dc.date.accessioned.none.fl_str_mv	2021-10-04T14:59:48Z
dc.date.available.none.fl_str_mv	2021-10-04T14:59:48Z
dc.date.issued.none.fl_str_mv	2021-09-29
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
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dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/80363 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.references.spa.fl_str_mv	S. Amaral, A. A. Gavlak, M. I. S. Escada, and A. M. V. Monteiro, “Using remote sensing and census tract data to improve representation of population spatial distribution: Case studies in the Brazilian Amazon,” Population and Environment, vol. 34, no. 1, pp. 142–170, sep 2012. [Online]. Available: https://link-springer-com.ezproxy.unal.edu.co/article/10.1007/s11111-012-0168-2 Eurostat, “Glossary:Population grid cell - Statistics Explained.” [Onli- ne]. Available: https://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary: Population{\ }grid{\ }cell “Population grids - Statistics Explained.” [Online]. Available: https://ec.europa. eu/eurostat/statistics-explained/index.php/Population{\ }grids C. Robinson, F. Hohman, and B. Dilkina, “A deep learning approach for population esti- mation from satellite imagery,” in Proceedings of the 1st ACM SIGSPATIAL Workshop on Geospatial Humanities, GeoHumanities 2017. Association for Computing Machi- nery, Inc, nov 2017, pp. 47–54. M. Ferguson, R. Ak, Y.-T. Lee, and K. Law, “Automatic localization of casting defects with convolutional neural networks,” 2017, pp. 1726–1735. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-Based Learning Applied to Document Recognition,” in Proceedings of the IEEE, vol. 86, no. 11, 1998, pp. 2278–2324. [Online]. Available: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.42.7665 N. M. Short, U. S. N. Aeronautics, S. A. Scientific, T. I. Branch, E. R. R. S. A. C. (U.S.), and G. S. F. Center, The Landsat Tutorial Workbook: Basics of Satellite Remote Sensing, ser. NASA reference publication. National Aeronautics and Space Administration, Scientific and Technical Information Branch, 1982. [Online]. Available: https://books.google.com.co/books?id=9RsrAAAAIAAJ M. A. Wulder, T. R. Loveland, D. P. Roy, C. J. Crawford, J. G. Masek, C. E. Wood- cock, R. G. Allen, M. C. Anderson, A. S. Belward, W. B. Cohen, J. Dwyer, A. Erb, F. Gao, P. Griffiths, D. Helder, T. Hermosilla, J. D. Hipple, P. Hostert, M. J. Hughes, J. Huntington, D. M. Johnson, R. Kennedy, A. Kilic, Z. Li, L. Lymburner, J. McCorkel, N. Pahlevan, T. A. Scambos, C. Schaaf, J. R. Schott, Y. Sheng, J. Storey, E. Vermote, J. Vogelmann, J. C. White, R. H. Wynne, and Z. Zhu, “Current status of Landsat pro- gram, science, and applications,” Remote Sensing of Environment, vol. 225, pp. 127–147, may 2019. E. European Space Agency, Sentinel-2 User Handbook. European Space Agency, 2015. P. Doupe, E. Bruzelius, J. Faghmous, and S. G. Ruchman, “Equitable Development through Deep Learning: The Case of Sub-National Population Density Estimation,” in Proceedings of the 7th Annual Symposium on Computing for Development, ser. ACM DEV ’16. New York, NY, USA: Association for Computing Machinery, 2016. [Online]. Available: https://doi.org/10.1145/3001913.3001921 F. Batista e Silva, J. Gallego, and C. Lavalle, “A high-resolution population grid map for Europe,” Journal of Maps, vol. 9, no. 1, pp. 16–28, 2013. L. Wang and X. Li, “Population estimation with remote sensing,” in Comprehensive Remote Sensing. Elsevier, jan 2017, vol. 1-9, pp. 59–66. R. C. Taragi, K. S. Bisht, and B. S. Sokhi, “Generating population census data through aerial remote sensing,” Journal of the Indian Society of Remote Sensing, vol. 22, no. 3, pp. 131–138, sep 1994. [Online]. Available: https: //link-springer-com.ezproxy.unal.edu.co/article/10.1007/BF03024774 K. Karume, C. Schmidt, K. Kundert, M. E. Bagula, B. F. Safina, R. Schomacker, D. Ganza, O. Azanga, C. Nfundiko, N. Karume, and G. N. Mushagalusa, “Use of Remote Sensing for Population Number Determination,” The Open Access Journal of Science and Technology, vol. 05, no. 03, 2017. B.-g. Zhang, “Application of remote sensing technology to population estimation,” Chinese Geographical Science, vol. 13, no. 3, pp. 267–271, sep 2003. [Online]. Available: https://link-springer-com.ezproxy.unal.edu.co/article/10.1007/s11769-003-0029-0 A. Sorichetta, G. M. Hornby, F. R. Stevens, A. E. Gaughan, C. Linard, and A. J. Tatem, “High-resolution gridded population datasets for Latin America and the Caribbean in 2010, 2015, and 2020,” Scientific Data, vol. 2, sep 2015. S. Zhao, Y. Liu, R. Zhang, and B. Fu, “China’s population spatialization based on three machine learning models,” Journal of Cleaner Production, vol. 256, p. 120644, may 2020. F. HORTON, “Remote sensing techniques and urban acquisition,” ESTESJ, SEN- GERL(eds.). Remote Sensing Techniques, pp. 243–276, 1974. E. C. B. L. F. Curtis, “Introduction to environmental remote sensing,” Chapman and Hall Ltd, pp. 238–241, 1986. A. E. Ayila, B. Paul Babatunde, and J. P. Yohanna, “Population estimation and census track demarcation in Hwolshe, Plateau State, Nigeria: A geospatial approach,” Remote Sensing Applications: Society and Environment, vol. 10, pp. 183–189, apr 2018. C. T. Lloyd, A. Sorichetta, and A. J. Tatem, “Data Descriptor: High resolution global gridded data for use in population studies,” Scientific Data, vol. 4, no. 1, pp. 1–17, jan 2017. [Online]. Available: www.nature.com/sdata/ A. Dmowska and T. F. Stepinski, “A high resolution population grid for the contermi- nous United States: The 2010 edition,” Computers, Environment and Urban Systems, vol. 61, pp. 13–23, jan 2017. Q. Yuan, H. Shen, T. Li, Z. Li, S. Li, Y. Jiang, H. Xu, W. Tan, Q. Yang, J. Wang, J. Gao, and L. Zhang, “Deep learning in environmental remote sensing: Achievements and challenges,” Remote Sensing of Environment, vol. 241, p. 111716, may 2020. M. Castelluccio, G. Poggi, C. Sansone, and L. Verdoliva, “Land Use Classification in Remote Sensing Images by Convolutional Neural Networks,” aug 2015. [Online]. Available: http://arxiv.org/abs/1508.00092 Karen Simonyan, Andrew Zisserman, “Very Deep Convolutional Networks for large- scale image recognition,” American Journal of Health-System Pharmacy, vol. 75, no. 6, pp. 398–406, 2018. DANE, “Guía para la anonimización de bases de datos en el Sistema Estadístico Nacional,” Bogotá, D.C., p. 72, 2018. G. Chander, B. L. Markham, and D. L. Helder, “Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors,” Remote Sensing of Environment, vol. 113, no. 5, pp. 893–903, 2009. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0034425709000169 A. H. Bagour, “Probability Proportional to sise Sampling,” Ph.D. dissertation, Oklaho- ma State University, 2004. C. Shorten and T. M. Khoshgoftaar, “A survey on Image Data Augmentation for Deep Learning,” Journal of Big Data, vol. 6, no. 1, p. 60, 2019. [Online]. Available: https://doi.org/10.1186/s40537-019-0197-0 E. Bisong, Building Machine Learning and Deep Learning Models on Google Cloud Plat- form: A Comprehensive Guide for Beginners, 2019. K. Jordahl, J. V. den Bossche, M. Fleischmann, J. Wasserman, J. McBride, J. Gerard, J. Tratner, M. Perry, A. G. Badaracco, C. Farmer, G. A. Hjelle, A. D. Snow, M. Cochran, S. Gillies, L. Culbertson, M. Bartos, N. Eubank, Maxalbert, A. Bilogur, S. Rey, C. Ren, D. Arribas-Bel, L. Wasser, L. J. Wolf, M. Journois, J. Wilson, A. Greenhall, C. Holdgraf, Filipe, and F. Leblanc, “geopandas/geopandas: v0.8.1,” 2020. [Online]. Available: https://doi.org/10.5281/zenodo.3946761 T. Pandas development team, “pandas-dev/pandas: Pandas,” 2020. [Online]. Available: https://doi.org/10.5281/zenodo.3509134 QGIS Development Team, QGIS Geographic Information System, Open Source Geospatial Foundation, 2009. [Online]. Available: http://qgis.org M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. 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dc.rights.spa.fl_str_mv	Derechos reservados al autor
dc.rights.coar.fl_str_mv	http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv	Reconocimiento 4.0 Internacional
dc.rights.uri.spa.fl_str_mv	http://creativecommons.org/licenses/by/4.0/
dc.rights.accessrights.spa.fl_str_mv	info:eu-repo/semantics/openAccess
rights_invalid_str_mv	Reconocimiento 4.0 Internacional Derechos reservados al autor http://creativecommons.org/licenses/by/4.0/ http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv	openAccess
dc.format.extent.spa.fl_str_mv	XVI, 46 páginas
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dc.coverage.city.none.fl_str_mv	Bogotá
dc.coverage.country.none.fl_str_mv	Colombia
dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Bogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y Computación
dc.publisher.department.spa.fl_str_mv	Departamento de Ingeniería de Sistemas e Industrial
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ingeniería
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Bogotá
institution	Universidad Nacional de Colombia
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spelling	Reconocimiento 4.0 InternacionalDerechos reservados al autorhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Niño Vásquez, Luís Fernando64a4b2d78f9ccaf1b5e2791aff382784Montero Leguizamón, Aníbalc47eb97593637c6ede7811bb39551cf2LABORATORIO DE INVESTIGACIÓN EN SISTEMAS INTELIGENTES - LISI2021-10-04T14:59:48Z2021-10-04T14:59:48Z2021-09-29https://repositorio.unal.edu.co/handle/unal/80363Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, fotografías, mapas, tablasSaber cuántas personas viven en un área determinada y saber en dónde habitan específicamente, son preguntas tradicionalmente abordadas desde la Demografía. El presente trabajo plantea la alternativa de utilizar imágenes satelitales para predecir el número de habitantes a partir de mallas de población. Se extrajo un conjunto de imágenes de Landsat 8, a partir de un diseño muestral proporcional al tamaño (PPS) aplicado sobre una malla de población censal del 2018 en Colombia. Se entrenó la arquitectura LeNet-5 modificada para realizar regresión sobre el número de habitantes por celda a partir del conjunto de imágenes obtenido. Se realizaron pruebas del modelado sobre una submuestra de la malla de población en Colombia y sobre la malla de población correspondiente a los municipios que componen el área metropolitana de Bogotá en 2018, arrojando MAEs de 947,8 y 1.181,9, respectivamente, igualando e incluso superando los resultados encontrados en el estado del arte. (Texto tomado de la fuente)Knowing how many people live in an area and knowing where they live specifically are questions commonly approached through Demography. The present work proposes the using of satellite images to predict the number of inhabitants based on population grids as an alternative approach. A Landsat 8 images dataset was generated using a Probability Proportional to Size (PPS) sample extracted on a 2018 census population grid in Colombia. A LeNet-5 architecture was modified to predict the number of inhabitants per cell and trained with the previous image dataset obtained. The trained model was tested with a subsample of the population grid in Colombia and the population grid corresponding to the municipalities of the Bogotá metropolitan area in 2018. The model reached MAEs of 947.8 and 1181.9, respectively. These results equal and even exceed the performance found in the state of the art.MaestríaMagíster en Ingeniería - Ingeniería de Sistemas y ComputaciónSe abordó una estrategia de investigación cuantitativa. Tal y como se relacionan los conceptos mencionados en el título y el objetivo general de la investigación, y en cuanto a la definición operacional de las variables implicadas en la investigación, se modeló la malla de población en el área metropolitana de Bogotá a partir de las características detectadas en las imágenes de sensores remotos a través de CNN. se adoptó un tipo de investigación no experimental y con significación temporal de tipo transversal.Sistemas InteligentesXVI, 46 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y ComputaciónDepartamento de Ingeniería de Sistemas e IndustrialFacultad de IngenieríaUniversidad Nacional de Colombia - Sede Bogotá000 - Ciencias de la computación, información y obras generalesRedes neuronalesNeural networksMalla de PoblaciónSensores RemotosAprendizaje ProfundoRedes Neuronales ConvolucionalesPopulation gridRemote SensingDeep LearningConvolutional Neural NetworksProyección demográficaPopulation projectionsAplicación de una red neuronal convolucional para la predicción de mallas de población en el área metropolitana de BogotáPredicting a population grid in the Bogotá metropolitan area, based on convolutional neural networksTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMBogotáColombiaS. Amaral, A. A. Gavlak, M. I. S. Escada, and A. M. V. Monteiro, “Using remote sensing and census tract data to improve representation of population spatial distribution: Case studies in the Brazilian Amazon,” Population and Environment, vol. 34, no. 1, pp. 142–170, sep 2012. [Online]. Available: https://link-springer-com.ezproxy.unal.edu.co/article/10.1007/s11111-012-0168-2Eurostat, “Glossary:Population grid cell - Statistics Explained.” [Onli- ne]. Available: https://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary: Population{\ }grid{\ }cell“Population grids - Statistics Explained.” [Online]. Available: https://ec.europa. eu/eurostat/statistics-explained/index.php/Population{\ }gridsC. Robinson, F. Hohman, and B. Dilkina, “A deep learning approach for population esti- mation from satellite imagery,” in Proceedings of the 1st ACM SIGSPATIAL Workshop on Geospatial Humanities, GeoHumanities 2017. Association for Computing Machi- nery, Inc, nov 2017, pp. 47–54.M. Ferguson, R. Ak, Y.-T. Lee, and K. Law, “Automatic localization of casting defects with convolutional neural networks,” 2017, pp. 1726–1735.Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-Based Learning Applied to Document Recognition,” in Proceedings of the IEEE, vol. 86, no. 11, 1998, pp. 2278–2324. [Online]. Available: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.42.7665N. M. Short, U. S. N. Aeronautics, S. A. Scientific, T. I. Branch, E. R. R. S. A. C. (U.S.), and G. S. F. Center, The Landsat Tutorial Workbook: Basics of Satellite Remote Sensing, ser. NASA reference publication. National Aeronautics and Space Administration, Scientific and Technical Information Branch, 1982. [Online]. Available: https://books.google.com.co/books?id=9RsrAAAAIAAJM. A. Wulder, T. R. Loveland, D. P. Roy, C. J. Crawford, J. G. Masek, C. E. Wood- cock, R. G. Allen, M. C. Anderson, A. S. Belward, W. B. Cohen, J. Dwyer, A. Erb, F. Gao, P. Griffiths, D. Helder, T. Hermosilla, J. D. Hipple, P. Hostert, M. J. Hughes, J. Huntington, D. M. Johnson, R. Kennedy, A. Kilic, Z. Li, L. Lymburner, J. McCorkel, N. Pahlevan, T. A. Scambos, C. Schaaf, J. R. Schott, Y. Sheng, J. Storey, E. Vermote, J. Vogelmann, J. C. White, R. H. Wynne, and Z. Zhu, “Current status of Landsat pro- gram, science, and applications,” Remote Sensing of Environment, vol. 225, pp. 127–147, may 2019.E. European Space Agency, Sentinel-2 User Handbook. European Space Agency, 2015.P. Doupe, E. Bruzelius, J. Faghmous, and S. G. Ruchman, “Equitable Development through Deep Learning: The Case of Sub-National Population Density Estimation,” in Proceedings of the 7th Annual Symposium on Computing for Development, ser. ACM DEV ’16. New York, NY, USA: Association for Computing Machinery, 2016. [Online]. Available: https://doi.org/10.1145/3001913.3001921F. Batista e Silva, J. Gallego, and C. Lavalle, “A high-resolution population grid map for Europe,” Journal of Maps, vol. 9, no. 1, pp. 16–28, 2013.L. Wang and X. Li, “Population estimation with remote sensing,” in Comprehensive Remote Sensing. Elsevier, jan 2017, vol. 1-9, pp. 59–66.R. C. Taragi, K. S. Bisht, and B. S. Sokhi, “Generating population census data through aerial remote sensing,” Journal of the Indian Society of Remote Sensing, vol. 22, no. 3, pp. 131–138, sep 1994. [Online]. Available: https: //link-springer-com.ezproxy.unal.edu.co/article/10.1007/BF03024774K. Karume, C. Schmidt, K. Kundert, M. E. Bagula, B. F. Safina, R. Schomacker, D. Ganza, O. Azanga, C. Nfundiko, N. Karume, and G. N. Mushagalusa, “Use of Remote Sensing for Population Number Determination,” The Open Access Journal of Science and Technology, vol. 05, no. 03, 2017.B.-g. Zhang, “Application of remote sensing technology to population estimation,” Chinese Geographical Science, vol. 13, no. 3, pp. 267–271, sep 2003. [Online]. Available: https://link-springer-com.ezproxy.unal.edu.co/article/10.1007/s11769-003-0029-0A. Sorichetta, G. M. Hornby, F. R. Stevens, A. E. Gaughan, C. Linard, and A. J. Tatem, “High-resolution gridded population datasets for Latin America and the Caribbean in 2010, 2015, and 2020,” Scientific Data, vol. 2, sep 2015.S. Zhao, Y. Liu, R. Zhang, and B. Fu, “China’s population spatialization based on three machine learning models,” Journal of Cleaner Production, vol. 256, p. 120644, may 2020.F. HORTON, “Remote sensing techniques and urban acquisition,” ESTESJ, SEN- GERL(eds.). Remote Sensing Techniques, pp. 243–276, 1974.E. C. B. L. F. Curtis, “Introduction to environmental remote sensing,” Chapman and Hall Ltd, pp. 238–241, 1986.A. E. Ayila, B. Paul Babatunde, and J. P. Yohanna, “Population estimation and census track demarcation in Hwolshe, Plateau State, Nigeria: A geospatial approach,” Remote Sensing Applications: Society and Environment, vol. 10, pp. 183–189, apr 2018.C. T. Lloyd, A. Sorichetta, and A. J. Tatem, “Data Descriptor: High resolution global gridded data for use in population studies,” Scientific Data, vol. 4, no. 1, pp. 1–17, jan 2017. [Online]. Available: www.nature.com/sdata/A. Dmowska and T. F. Stepinski, “A high resolution population grid for the contermi- nous United States: The 2010 edition,” Computers, Environment and Urban Systems, vol. 61, pp. 13–23, jan 2017.Q. Yuan, H. Shen, T. Li, Z. Li, S. Li, Y. Jiang, H. Xu, W. Tan, Q. Yang, J. Wang, J. Gao, and L. Zhang, “Deep learning in environmental remote sensing: Achievements and challenges,” Remote Sensing of Environment, vol. 241, p. 111716, may 2020.M. Castelluccio, G. Poggi, C. Sansone, and L. Verdoliva, “Land Use Classification in Remote Sensing Images by Convolutional Neural Networks,” aug 2015. [Online]. 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GVyZWNob3MgZGUgYXV0b3IgcXVlIGNvbmxsZXZlIGxhIGRpc3RyaWJ1Y2nDs24gZGUgZXN0b3MgYXJjaGl2b3MgeSBtZXRhZGF0b3MuCkFsIGhhY2VyIGNsaWMgZW4gZWwgc2lndWllbnRlIGJvdMOzbiwgdXN0ZWQgaW5kaWNhIHF1ZSBlc3TDoSBkZSBhY3VlcmRvIGNvbiBlc3RvcyB0w6lybWlub3MuCgpVTklWRVJTSURBRCBOQUNJT05BTCBERSBDT0xPTUJJQSAtIMOabHRpbWEgbW9kaWZpY2FjacOzbiAyNy8yMC8yMDIwCg==

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