Mapping dispersed houses in rural areas of Colombia by exploiting planet satellite images with convolutional neural networks
The Sustainable Development Goal (SDG) number 11 aims at making cities and human settlements more inclusive, safe, resilient, and sustainable. Complying with SDG 11 is a difficult task, especially when considering rural settlements where: (i) population settles in a dispersed manner; and (ii) geogra...
- Autores:
-
Arrechea-Castillo, Darwin Alexis
Solano-Correa, Yady Tatiana
Muñoz-Ordóñez, Julian
Pencue-Fierro, Edgar Leonairo
Sánchez-Barrera, Estiven
- Tipo de recurso:
- Fecha de publicación:
- 2023
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/12734
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/12734
- Palabra clave:
- Rural settelment
Deep learning
Remote sensing
PlanetScope
SDGs
LEMB
- Rights
- closedAccess
- License
- http://purl.org/coar/access_right/c_14cb
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| dc.title.spa.fl_str_mv |
Mapping dispersed houses in rural areas of Colombia by exploiting planet satellite images with convolutional neural networks |
| title |
Mapping dispersed houses in rural areas of Colombia by exploiting planet satellite images with convolutional neural networks |
| spellingShingle |
Mapping dispersed houses in rural areas of Colombia by exploiting planet satellite images with convolutional neural networks Rural settelment Deep learning Remote sensing PlanetScope SDGs LEMB |
| title_short |
Mapping dispersed houses in rural areas of Colombia by exploiting planet satellite images with convolutional neural networks |
| title_full |
Mapping dispersed houses in rural areas of Colombia by exploiting planet satellite images with convolutional neural networks |
| title_fullStr |
Mapping dispersed houses in rural areas of Colombia by exploiting planet satellite images with convolutional neural networks |
| title_full_unstemmed |
Mapping dispersed houses in rural areas of Colombia by exploiting planet satellite images with convolutional neural networks |
| title_sort |
Mapping dispersed houses in rural areas of Colombia by exploiting planet satellite images with convolutional neural networks |
| dc.creator.fl_str_mv |
Arrechea-Castillo, Darwin Alexis Solano-Correa, Yady Tatiana Muñoz-Ordóñez, Julian Pencue-Fierro, Edgar Leonairo Sánchez-Barrera, Estiven |
| dc.contributor.author.none.fl_str_mv |
Arrechea-Castillo, Darwin Alexis Solano-Correa, Yady Tatiana Muñoz-Ordóñez, Julian Pencue-Fierro, Edgar Leonairo Sánchez-Barrera, Estiven |
| dc.subject.keywords.spa.fl_str_mv |
Rural settelment Deep learning Remote sensing PlanetScope SDGs |
| topic |
Rural settelment Deep learning Remote sensing PlanetScope SDGs LEMB |
| dc.subject.armarc.none.fl_str_mv |
LEMB |
| description |
The Sustainable Development Goal (SDG) number 11 aims at making cities and human settlements more inclusive, safe, resilient, and sustainable. Complying with SDG 11 is a difficult task, especially when considering rural settlements where: (i) population settles in a dispersed manner; and (ii) geography complexity and social dynamics of the area make it difficult to monitor and capture data. One example of such areas can be found in the South-West of Colombia, in the Las Piedras River sub-basin. The National Administrative Department of Statistics in Colombia (DANE in Spanish) aims at mapping the population and houses in dispersed and difficult-to-access rural settlements in an accurate and continuous way. Nevertheless, there are several difficulties (derived from the in-situ way of collecting the data) that prevent such data from being generated. This research presents a methodology to carry out an updated mapping of rural areas with high spatial resolution data coming from PlanetScope (3m). Such a mapping considers the dynamics of housing growth, focusing on dispersed and difficult-to-access rural settlements. To this aim, Convolutional Neural Networks (CNNs) are used together with PlanetScope data, allowing to account for average houses size (≥12����2 ) in the study area. Preliminary results show a detection accuracy above 95%, in average, according to geography complexity |
| publishDate |
2023 |
| dc.date.issued.none.fl_str_mv |
2023-06-15 |
| dc.date.accessioned.none.fl_str_mv |
2024-09-12T14:03:23Z |
| dc.date.available.none.fl_str_mv |
2024-09-12T14:03:23Z |
| dc.date.submitted.none.fl_str_mv |
2024-09-11 |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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info:eu-repo/semantics/article |
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info:eu-repo/semantics/publishedVersion |
| dc.type.spa.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_c94f |
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publishedVersion |
| dc.identifier.citation.spa.fl_str_mv |
D.A. Arrechea-Castillo; Y. T. Solano-Correa; J.F. Muñoz-Ordóñez; E.L. Pencue-Fierro; E. Sánchez-Barrera, "Mapping dispersed houses in rural areas of Colombia by exploiting planet satellite images with convolutional neural networks," in Proc. SPIE 15525, Geospatial Informatics XIII, 1252503 (15 June 2023). DOI: https://doi.org/10.1117/12.2664029. |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12585/12734 |
| dc.identifier.doi.none.fl_str_mv |
10.1117/12.2664029 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Tecnológica de Bolívar |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Universidad Tecnológica de Bolívar |
| identifier_str_mv |
D.A. Arrechea-Castillo; Y. T. Solano-Correa; J.F. Muñoz-Ordóñez; E.L. Pencue-Fierro; E. Sánchez-Barrera, "Mapping dispersed houses in rural areas of Colombia by exploiting planet satellite images with convolutional neural networks," in Proc. SPIE 15525, Geospatial Informatics XIII, 1252503 (15 June 2023). DOI: https://doi.org/10.1117/12.2664029. 10.1117/12.2664029 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
| url |
https://hdl.handle.net/20.500.12585/12734 |
| dc.language.iso.spa.fl_str_mv |
eng |
| language |
eng |
| dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_14cb |
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info:eu-repo/semantics/closedAccess |
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closedAccess |
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http://purl.org/coar/access_right/c_14cb |
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9 páginas |
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application/pdf |
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Cartagena de Indias |
| dc.publisher.faculty.spa.fl_str_mv |
Ciencias Básicas |
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SPIE 15525, Geospatial Informatics XIII |
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Universidad Tecnológica de Bolívar |
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Arrechea-Castillo, Darwin Alexis93ac81fe-17eb-4bd1-a2fc-f39f13cc9af2Solano-Correa, Yady Tatianac3d85b81-c6f5-4ad0-80dc-65e4cf4283b1Muñoz-Ordóñez, Julian657e1907-a2b8-4a5c-aa0d-7adbef3a4cc1Pencue-Fierro, Edgar Leonairo6964c8f9-622b-4193-9015-e2dbfaf05127Sánchez-Barrera, Estiven338ae022-97b6-407e-b53b-4fe8139105b72024-09-12T14:03:23Z2024-09-12T14:03:23Z2023-06-152024-09-11D.A. Arrechea-Castillo; Y. T. Solano-Correa; J.F. Muñoz-Ordóñez; E.L. Pencue-Fierro; E. Sánchez-Barrera, "Mapping dispersed houses in rural areas of Colombia by exploiting planet satellite images with convolutional neural networks," in Proc. SPIE 15525, Geospatial Informatics XIII, 1252503 (15 June 2023). DOI: https://doi.org/10.1117/12.2664029.https://hdl.handle.net/20.500.12585/1273410.1117/12.2664029Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarThe Sustainable Development Goal (SDG) number 11 aims at making cities and human settlements more inclusive, safe, resilient, and sustainable. Complying with SDG 11 is a difficult task, especially when considering rural settlements where: (i) population settles in a dispersed manner; and (ii) geography complexity and social dynamics of the area make it difficult to monitor and capture data. One example of such areas can be found in the South-West of Colombia, in the Las Piedras River sub-basin. The National Administrative Department of Statistics in Colombia (DANE in Spanish) aims at mapping the population and houses in dispersed and difficult-to-access rural settlements in an accurate and continuous way. Nevertheless, there are several difficulties (derived from the in-situ way of collecting the data) that prevent such data from being generated. This research presents a methodology to carry out an updated mapping of rural areas with high spatial resolution data coming from PlanetScope (3m). Such a mapping considers the dynamics of housing growth, focusing on dispersed and difficult-to-access rural settlements. To this aim, Convolutional Neural Networks (CNNs) are used together with PlanetScope data, allowing to account for average houses size (≥12����2 ) in the study area. Preliminary results show a detection accuracy above 95%, in average, according to geography complexity9 páginasapplication/pdfengSPIE 15525, Geospatial Informatics XIIIMapping dispersed houses in rural areas of Colombia by exploiting planet satellite images with convolutional neural networksinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_c94fhttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1Rural settelmentDeep learningRemote sensingPlanetScopeSDGsLEMBinfo:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbCartagena de IndiasCiencias BásicasInvestigadoresUnited Nations., “The Global Movement for Our Children’s FThe Global Challenge for Government Transparency: The Sustainable Development Goals (SDG) 2030 Agendauture- World Top 20 Project,” Educate Every Child on the Planet: The World Top 20 Project, 19 October 2022, <https://worldtop20.org/global-movement> (19 October 2022 ).Yamasaki, K. and Yamada, T., “A framework to assess the local implementation of Sustainable Development Goal 11,” Sust. Cities Soc. 84, 104002 (2022).Aquilino, M., Adamo, M., Blonda, P., Barbanente, A. and Tarantino, C., “Improvement of a Dasymetric Method for Implementing Sustainable Development Goal 11 Indicators at an Intra-Urban Scale,” 14, Remote Sensing 13(14), 2835 (2021).Ruiz O., D. M., Idrobo M., J. P., Otero S., J. D. and Figueroa C., A., “Effects of Productive Activities on the Water Quality for Human Consumption in an Andean Basin, a Case Study,” Revista Internacional de Contaminación Ambiental 33(3), 361–375 (2017).Roncancio, D. J., Cutter, S. L. and Nardocci, A. C., “Social vulnerability in Colombia,” Int. J. Disaster Risk Reduct. 50, 101872 (2020).“Departamento Administrativo Nacional de Estadistica (DANE).”, <https://www.dane.gov.co/> (11 April 2023 ).Departamento Administrativo Nacional de Eestadística (DANE)., “Censo Nacional de Población y Vivienda 2018,” DANE - Información Para Todos, <https://www.dane.gov.co/index.php/estadisticas-por-tema/demografia-ypoblacion/ censo-nacional-de-poblacion-y-vivenda-2018> (30 October 2022 ).Kaplan, G. and Kaplan, O., “PlanetScope Imagery for Extracting Building Inventory Information,” 1, Environmental Sciences Proceedings 5(1), 19 (2020).Chuvieco, E., [Fundamentals of satellite remote sensing: an environmental approach], Taylor & Francis (2016).Camps-Valls, G., Tuia, D., Zhu, X. X. and Reichstein, M., [Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science and Geosciences, 1st ed.], Wiley (2021).http://purl.org/coar/resource_type/c_c94fORIGINAL2023-C-Mapping dispersed houses in rural areas of Colombia by exploiting planet satellite images with convolutional neural networks_1252503.pdf2023-C-Mapping dispersed houses in rural areas of Colombia by exploiting planet satellite images with convolutional neural networks_1252503.pdfapplication/pdf981097https://repositorio.utb.edu.co/bitstream/20.500.12585/12734/1/2023-C-Mapping%20dispersed%20houses%20in%20rural%20areas%20of%20Colombia%20by%20exploiting%20planet%20satellite%20images%20with%20convolutional%20neural%20networks_1252503.pdfe1dd6457871246f9245c827a05d4271fMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-83182https://repositorio.utb.edu.co/bitstream/20.500.12585/12734/2/license.txte20ad307a1c5f3f25af9304a7a7c86b6MD52TEXT2023-C-Mapping dispersed houses in 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