Land cover classification of Andean sub-basins in Colombia based on Sentinel-2 satellite images and deep learning
The Las Piedras River sub-basin, located in the department of Cauca, Colombia, is very important for the region, especially for the capital (Popayán). This is because this sub-basin contributes around 68.17% of the water supply for the city. To guarantee continuity of this resource, good management...
- Autores:
-
Arrechea-Castillo, Darwin Alexis
Solano-Correa, Yady Tatiana
Muñóz-Ordóñez, Julián Fernando
Camacho-De Angulo, Yineth Viviana
Sánchez-Barrera, Estiven
Figueroa-Casas, Apolinar
Pencue-Fierro, Edgar Leonairo
- 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:20.500.12585/12735
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/12735
- Palabra clave:
- Deep learning
Convolutional Neural Networks (CNNs)
Remote Sensing
Land Use and Land Cover
Sentinel-2
LEMB
- Rights
- closedAccess
- License
- http://purl.org/coar/access_right/c_14cb
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Land cover classification of Andean sub-basins in Colombia based on Sentinel-2 satellite images and deep learning |
| title |
Land cover classification of Andean sub-basins in Colombia based on Sentinel-2 satellite images and deep learning |
| spellingShingle |
Land cover classification of Andean sub-basins in Colombia based on Sentinel-2 satellite images and deep learning Deep learning Convolutional Neural Networks (CNNs) Remote Sensing Land Use and Land Cover Sentinel-2 LEMB |
| title_short |
Land cover classification of Andean sub-basins in Colombia based on Sentinel-2 satellite images and deep learning |
| title_full |
Land cover classification of Andean sub-basins in Colombia based on Sentinel-2 satellite images and deep learning |
| title_fullStr |
Land cover classification of Andean sub-basins in Colombia based on Sentinel-2 satellite images and deep learning |
| title_full_unstemmed |
Land cover classification of Andean sub-basins in Colombia based on Sentinel-2 satellite images and deep learning |
| title_sort |
Land cover classification of Andean sub-basins in Colombia based on Sentinel-2 satellite images and deep learning |
| dc.creator.fl_str_mv |
Arrechea-Castillo, Darwin Alexis Solano-Correa, Yady Tatiana Muñóz-Ordóñez, Julián Fernando Camacho-De Angulo, Yineth Viviana Sánchez-Barrera, Estiven Figueroa-Casas, Apolinar Pencue-Fierro, Edgar Leonairo |
| dc.contributor.author.none.fl_str_mv |
Arrechea-Castillo, Darwin Alexis Solano-Correa, Yady Tatiana Muñóz-Ordóñez, Julián Fernando Camacho-De Angulo, Yineth Viviana Sánchez-Barrera, Estiven Figueroa-Casas, Apolinar Pencue-Fierro, Edgar Leonairo |
| dc.subject.keywords.spa.fl_str_mv |
Deep learning Convolutional Neural Networks (CNNs) Remote Sensing Land Use and Land Cover Sentinel-2 |
| topic |
Deep learning Convolutional Neural Networks (CNNs) Remote Sensing Land Use and Land Cover Sentinel-2 LEMB |
| dc.subject.armarc.none.fl_str_mv |
LEMB |
| description |
The Las Piedras River sub-basin, located in the department of Cauca, Colombia, is very important for the region, especially for the capital (Popayán). This is because this sub-basin contributes around 68.17% of the water supply for the city. To guarantee continuity of this resource, good management of the Water Ecosystem Services (WES) must be carried out. To this aim, periodic environmental assessments of the water resource in the region are necessary. Such Environmental Assessment WES (EAWES) is possible when an accurate and up-to-date land cover map is available. However, obtaining such a product is quite complex due to the heterogeneous conditions both in the land cover and orography of the studied region. Another impacting factor is the weather conditions of the region, that make it difficult to access the areas and/or to acquire information for land cover mapping. This research proposes a robust model, based on deep learning and Sentinel2 satellite images, able to perform a land cover classification with reliable accuracy (>90%) at a low computational cost. A variant of a LeNet convolutional neural network has been used together with features extracted from original spectral bands, radiometric indices and a digital elevation map. Preliminary results show an overall accuracy of 95.49% in the training data and 96.51% in the validation one. |
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2023 |
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2023-06-15 |
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2024-09-12T14:03:55Z |
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2024-09-12T14:03:55Z |
| 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 |
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http://purl.org/coar/resource_type/c_c94f |
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publishedVersion |
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D.A. Arrechea-Castillo; Y. T. Solano-Correa; J.F. Muñoz-Ordóñez; Y.V. Camacho-De Angulo; E. Sánchez-Barrera; A. Figueroa-Casas; E.L. Pencue-Fierro, "Land cover classification of Andean sub-basins in Colombia based on Sentinel-2 satellite images and deep learning," in Proc. SPIE 15525, Geospatial Informatics XIII, 1252505 (15 June 2023). DOI: https://doi.org/10.1117/12.2664340. |
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https://hdl.handle.net/20.500.12585/12735 |
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10.1117/12.2664340 |
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Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
| identifier_str_mv |
D.A. Arrechea-Castillo; Y. T. Solano-Correa; J.F. Muñoz-Ordóñez; Y.V. Camacho-De Angulo; E. Sánchez-Barrera; A. Figueroa-Casas; E.L. Pencue-Fierro, "Land cover classification of Andean sub-basins in Colombia based on Sentinel-2 satellite images and deep learning," in Proc. SPIE 15525, Geospatial Informatics XIII, 1252505 (15 June 2023). DOI: https://doi.org/10.1117/12.2664340. 10.1117/12.2664340 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
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https://hdl.handle.net/20.500.12585/12735 |
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eng |
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eng |
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9 páginas |
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application/pdf |
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Cartagena de Indias |
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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 Tatiana64432ee7-11fa-4bfb-b643-143125ef82c1Muñóz-Ordóñez, Julián Fernandob77c6955-e45e-4c6e-8e10-60c35c1ab1ccCamacho-De Angulo, Yineth Vivianadf97ad9f-47e5-45c8-922c-d1e2b12c9708Sánchez-Barrera, Estiven23cf36e6-e952-42a3-be21-c224536a65efFigueroa-Casas, Apolinar655429a4-4738-41ad-ae58-f63c237f68baPencue-Fierro, Edgar Leonairo6964c8f9-622b-4193-9015-e2dbfaf051272024-09-12T14:03:55Z2024-09-12T14:03:55Z2023-06-152024-09-11D.A. Arrechea-Castillo; Y. T. Solano-Correa; J.F. Muñoz-Ordóñez; Y.V. Camacho-De Angulo; E. Sánchez-Barrera; A. Figueroa-Casas; E.L. Pencue-Fierro, "Land cover classification of Andean sub-basins in Colombia based on Sentinel-2 satellite images and deep learning," in Proc. SPIE 15525, Geospatial Informatics XIII, 1252505 (15 June 2023). DOI: https://doi.org/10.1117/12.2664340.https://hdl.handle.net/20.500.12585/1273510.1117/12.2664340Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarThe Las Piedras River sub-basin, located in the department of Cauca, Colombia, is very important for the region, especially for the capital (Popayán). This is because this sub-basin contributes around 68.17% of the water supply for the city. To guarantee continuity of this resource, good management of the Water Ecosystem Services (WES) must be carried out. To this aim, periodic environmental assessments of the water resource in the region are necessary. Such Environmental Assessment WES (EAWES) is possible when an accurate and up-to-date land cover map is available. However, obtaining such a product is quite complex due to the heterogeneous conditions both in the land cover and orography of the studied region. Another impacting factor is the weather conditions of the region, that make it difficult to access the areas and/or to acquire information for land cover mapping. This research proposes a robust model, based on deep learning and Sentinel2 satellite images, able to perform a land cover classification with reliable accuracy (>90%) at a low computational cost. A variant of a LeNet convolutional neural network has been used together with features extracted from original spectral bands, radiometric indices and a digital elevation map. Preliminary results show an overall accuracy of 95.49% in the training data and 96.51% in the validation one.9 páginasapplication/pdfengSPIE 15525, Geospatial Informatics XIIILand cover classification of Andean sub-basins in Colombia based on Sentinel-2 satellite images and deep learninginfo: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_2df8fbb1Deep learningConvolutional Neural Networks (CNNs)Remote SensingLand Use and Land CoverSentinel-2LEMBinfo:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbCartagena de IndiasCiencias BásicasInvestigadoresOswald, S. M., Hollosi, B., Žuvela-Aloise, M., See, L., Guggenberger, S., Hafner, W., Prokop, G., Storch, A. and Schieder, W., “Using urban climate modelling and improved land use classifications to support climate change adaptation in urban environments: A case study for the city of Klagenfurt, Austria,” Urban Climate 31(100582), 16 (2020).Benhammou, Y., Alcaraz-Segura, D., Guirado, E., Khaldi, R., Achchab, B., Herrera, F. and Tabik, S., “Sentinel2GlobalLULC: A Sentinel-2 RGB image tile dataset for global land use/cover mapping with deep learning,” 1, Sci Data 9(1), 20 (2022).Carranza-García, M., García-Gutiérrez, J. and Riquelme, J. C., “A Framework for Evaluating Land Use and Land Cover Classification Using Convolutional Neural Networks,” 3, Remote Sensing 11(3), 274 (2019).Chuvieco, E., Li, J. and Yang, X., eds., [Advances in Earth Observation of Global Change, 1st ed.], Springer Netherlands, Dordrecht (2010).Smyth, T. A. G., Wilson, R., Rooney, P. and Yates, K. L., “Extent, accuracy and repeatability of bare sand and vegetation cover in dunes mapped from aerial imagery is highly variable,” Aeolian Research 56, 100799 (2022).Lilay, M. Y. and Taye, G. D., “Semantic segmentation model for land cover classification from satellite images in Gambella National Park, Ethiopia,” SN Appl. Sci. 5(76), 15 (2023).Yuh, Y. G., Tracz, W., Matthews, H. D. and Turner, S. E., “Application of machine learning approaches for land cover monitoring in northern Cameroon,” Ecological Informatics 74, 101955 (2023).Keshtkar, H., Voigt, W. and Alizadeh, E., “Land-cover classification and analysis of change using machine-learning classifiers and multi-temporal remote sensing imagery,” Arab J Geosci 10(154), 154 (2017).Pencue-Fierro, E. L., Solano-Correa, Y. T., Corrales-Muñoz, J. C. and Figueroa-Casas, A., “A Semi-Supervised Hybrid Approach for Multitemporal Multi-Region Multisensor Landsat Data Classification,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9(12), 5424–5435 (2016).Swetanisha, S., Panda, A. R. and Behera, D. K., “Land use/land cover classification using machine learning models,” 2, International Journal of Electrical and Computer Engineering (IJECE) 12(2), 2040–2046 (2022).Park, J., Lee, Y. and Lee, J., “Assessment of Machine Learning Algorithms for Land Cover Classification Using Remotely Sensed Data,” Sensors and Materials 33(11), 3885 (2021).Razafinimaro, A., Hajalalaina, A. R., Rakotonirainy, H. and Zafimarina, R., “Land cover classification based optical satellite images using machine learning algorithms,” 3, International Journal of Advances in Intelligent Informatics 8(3), 362–380 (2022).Simón Sánchez, A.-M., González-Piqueras, J., de la Ossa, L. and Calera, A., “Convolutional Neural Networks for Agricultural Land Use Classification from Sentinel-2 Image Time Series,” 21, Remote Sensing 14(21), 5373 (2022).Zhang, W., Tang, P., Corpetti, T. and Zhao, L., “WTS: A Weakly towards Strongly Supervised Learning Framework for Remote Sensing Land Cover Classification Using Segmentation Models,” 3, Remote Sensing 13(3), 394 (2021).Pedrayes, O. D., Lema, D. G., García, D. F., Usamentiaga, R. and Alonso, Á., “Evaluation of Semantic Segmentation Methods for Land Use with Spectral Imaging Using Sentinel-2 and PNOA Imagery,” 12, Remote Sensing 13(12), 2292 (2021).Kroupi, E., Kesa, M., Navarro-Sánchez, V. D., Saeed, S., Pelloquin, C., Alhaddad, B., Moreno, L., Soria-Frisch, A. and Ruffini, G., “Deep convolutional neural networks for land-cover classification with Sentinel-2 images,” JARS 13(2), 024525 (2019).J. Louis, O Devignot, and L. Pessiot., “Level-2A Algorithm Theoretical Basis Document,” Technical report, 78 (2021).López, I. D., Figueroa, A. and Corrales, J. C., “Multi-Dimensional Data Preparation: A Process to Support Vulnerability Analysis and Climate Change Adaptation,” IEEE Access 8, 87228–87242 (2020).Ren, H. and Feng, G., “Are soil-adjusted vegetation indices better than soil-unadjusted vegetation indices for aboveground green biomass estimation in arid and semi-arid grasslands?,” Grass and Forage Science 70(4), 611–619 (2015).Baret, F. and Guyot, G., “Potentials and limits of vegetation indices for LAI and APAR assessment,” Remote Sensing of Environment 35(2), 161–173 (1991).Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X. and Ferreira, L. G., “Overview of the radiometric and biophysical performance of the MODIS vegetation indices,” Remote Sensing of Environment 83(1), 195–213 (2002).Hunt Jr., E. R., Daughtry, C. S. T., Eitel, J. U. H. and Long, D. S., “Remote Sensing Leaf Chlorophyll Content Using a Visible Band Index,” Agronomy Journal 103(4), 1090–1099 (2011).Nouaim, W., Chakiri, S., Rambourg, D., Karaoui, I., Ettaqy, A., Chao, J., Allouza, M., Razoki, B., Yazidi, M. and El Hmidi, F., “Mapping the water erosion risk in the Lakhdar river basin (central High Atlas, Morocco),” Geology, Ecology, and Landscapes 3(1), 22–28 (2019).Gao, B., “NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space,” Remote Sensing of Environment 58(3), 257–266 (1996).Dogan, H. M., “Applications of remote sensing and Geographic Information Systems to assess ferrous minerals and iron oxide of Tokat province in Turkey,” International Journal of Remote Sensing 29(1), 221–233 (2008).Yan, C., Fan, X., Fan, J., Yu, L., Wang, N., Chen, L. and Li, X., “HyFormer: Hybrid Transformer and CNN for Pixel-Level Multispectral Image Land Cover Classification,” 4, International Journal of Environmental Research and Public Health 20(4), 3059 (2023).Lecun, Y., Bottou, L., Bengio, Y. and Haffner, P., “Gradient-based learning applied to document recognition,” Proceedings of the IEEE 86(11), 2278–2324 (1998).Yang, C., Rottensteiner, F. and Heipke, C., “CLASSIFICATION of LAND COVER and LAND USE BASED on CONVOLUTIONAL NEURAL NETWORKS,” presented at ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2018, 251–258.“The Sequential model | TensorFlow Core.”, TensorFlow, <bit.ly/3Fr9qPP> (15 March 2023 ).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).Phiri, D., Simwanda, M., Salekin, S., Nyirenda, V. R., Murayama, Y. and Ranagalage, M., “Sentinel-2 Data for Land Cover/Use Mapping: A Review,” 14, Remote Sensing 12(14), 2291 (2020).Essien, P., Figueiredo, C. A. O. B., Takahashi, H., Klutse, N. A. B., Wrasse, C. M., Afonso, J. M. de S., Quispe, D. P., Lomotey, S. O., Ayorinde, T. T., Sobral, J. H. A., Eghan, M. J., Sackey, S. S., Barros, D., Bilibio, A. V., Nkrumah, F. and Quagraine, K. A., “Intertropical Convergence Zone as the Possible Source Mechanism for Southward Propagating Medium-Scale Traveling Ionospheric Disturbances over South American Low-Latitude and Equatorial Region,” 11, Atmosphere 13(11), 15 (2022).European Space Agency (ESA)., “Copernicus Open Access Hub,” <https://scihub.copernicus.eu/dhus/#/home> (29 March 2023 ).Alaska Satellite Facility., “ASF Data Search,” <https://search.asf.alaska.edu/#/> (29 March 2023 ).Muñoz-Ordóñez, J., Cobos, C., Mendoza, M., Herrera-Viedma, E., Herrera, F. and Tabik, S., “Framework for the Training of Deep Neural Networks in TensorFlow Using Metaheuristics,” Intelligent Data Engineering and Automated Learning – IDEAL 2018, H. Yin, D. Camacho, P. Novais, and A. J. Tallón-Ballesteros, Eds., 801–811, Springer International Publishing, Cham (2018).Zanaga, D., Van De Kerchove, R., Daems, D., De Keersmaecker, W., Brockmann, C., Kirches, G., Wevers, J., Cartus, O., Santoro, M., Fritz, S., Lesiv, M., Herold, M., Tsendbazar, N.-E., Xu, P., Ramoino, F. and Arino, O., “ESA WorldCover 10 m 2021 v200” (2022).http://purl.org/coar/resource_type/c_c94fORIGINAL2023-C-Land cover classification of Andean sub-basins in Colombia based on Sentinel-2 satellite images and deep learning_1252505.pdf2023-C-Land cover classification of Andean sub-basins in Colombia based on Sentinel-2 satellite images and deep learning_1252505.pdfapplication/pdf895861https://repositorio.utb.edu.co/bitstream/20.500.12585/12735/1/2023-C-Land%20cover%20classification%20of%20Andean%20sub-basins%20in%20Colombia%20based%20on%20Sentinel-2%20satellite%20images%20and%20deep%20learning_1252505.pdf1d1b03bdf7c0447289e5881521395c35MD51LICENSElicense.txtlicense.txttext/plain; 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