Clasificación de zonas agrícolas en Colombia por medio de imágenes satelitales con redes neuronales profundas
ilustraciones, diagramas, fotografías, mapas, planos
- Autores:
-
Álvarez Montoya, Sebastián Felipe
- Tipo de recurso:
- Fecha de publicación:
- 2023
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/85836
- Palabra clave:
- 630 - Agricultura y tecnologías relacionadas::633 - Cultivos de campo y de plantación
620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
Aprendizaje automático
machine learning
Imágenes satelitales
Zonas agrícolas en Colombia
Aprendizaje por transferencia
Redes neuronales profundas
Satellite images
Agricultural zones in Colombia
Transfer learning
Deep neural networks
Sistema de información geográfica
Zona rural
Geographical information systems
Rural areas
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
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| dc.title.spa.fl_str_mv |
Clasificación de zonas agrícolas en Colombia por medio de imágenes satelitales con redes neuronales profundas |
| dc.title.translated.eng.fl_str_mv |
Classification of agricultural areas in Colombia through satellite images with deep neural networks |
| title |
Clasificación de zonas agrícolas en Colombia por medio de imágenes satelitales con redes neuronales profundas |
| spellingShingle |
Clasificación de zonas agrícolas en Colombia por medio de imágenes satelitales con redes neuronales profundas 630 - Agricultura y tecnologías relacionadas::633 - Cultivos de campo y de plantación 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Aprendizaje automático machine learning Imágenes satelitales Zonas agrícolas en Colombia Aprendizaje por transferencia Redes neuronales profundas Satellite images Agricultural zones in Colombia Transfer learning Deep neural networks Sistema de información geográfica Zona rural Geographical information systems Rural areas |
| title_short |
Clasificación de zonas agrícolas en Colombia por medio de imágenes satelitales con redes neuronales profundas |
| title_full |
Clasificación de zonas agrícolas en Colombia por medio de imágenes satelitales con redes neuronales profundas |
| title_fullStr |
Clasificación de zonas agrícolas en Colombia por medio de imágenes satelitales con redes neuronales profundas |
| title_full_unstemmed |
Clasificación de zonas agrícolas en Colombia por medio de imágenes satelitales con redes neuronales profundas |
| title_sort |
Clasificación de zonas agrícolas en Colombia por medio de imágenes satelitales con redes neuronales profundas |
| dc.creator.fl_str_mv |
Álvarez Montoya, Sebastián Felipe |
| dc.contributor.advisor.spa.fl_str_mv |
González Osorio, Fabio Augusto Ramos Pollán, Raúl |
| dc.contributor.author.spa.fl_str_mv |
Álvarez Montoya, Sebastián Felipe |
| dc.contributor.researchgroup.spa.fl_str_mv |
Mindlab |
| dc.subject.ddc.spa.fl_str_mv |
630 - Agricultura y tecnologías relacionadas::633 - Cultivos de campo y de plantación 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería |
| topic |
630 - Agricultura y tecnologías relacionadas::633 - Cultivos de campo y de plantación 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Aprendizaje automático machine learning Imágenes satelitales Zonas agrícolas en Colombia Aprendizaje por transferencia Redes neuronales profundas Satellite images Agricultural zones in Colombia Transfer learning Deep neural networks Sistema de información geográfica Zona rural Geographical information systems Rural areas |
| dc.subject.agrovoc.none.fl_str_mv |
Aprendizaje automático machine learning |
| dc.subject.proposal.spa.fl_str_mv |
Imágenes satelitales Zonas agrícolas en Colombia Aprendizaje por transferencia Redes neuronales profundas |
| dc.subject.proposal.eng.fl_str_mv |
Satellite images Agricultural zones in Colombia Transfer learning Deep neural networks |
| dc.subject.unesco.spa.fl_str_mv |
Sistema de información geográfica Zona rural |
| dc.subject.unesco.eng.fl_str_mv |
Geographical information systems Rural areas |
| description |
ilustraciones, diagramas, fotografías, mapas, planos |
| publishDate |
2023 |
| dc.date.issued.none.fl_str_mv |
2023 |
| dc.date.accessioned.none.fl_str_mv |
2024-04-02T00:30:16Z |
| dc.date.available.none.fl_str_mv |
2024-04-02T00:30:16Z |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Maestría |
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info:eu-repo/semantics/masterThesis |
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info:eu-repo/semantics/acceptedVersion |
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Text |
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http://purl.org/redcol/resource_type/TM |
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acceptedVersion |
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https://repositorio.unal.edu.co/handle/unal/85836 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
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https://repositorio.unal.edu.co/ |
| url |
https://repositorio.unal.edu.co/handle/unal/85836 https://repositorio.unal.edu.co/ |
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Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
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spa |
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spa |
| dc.relation.indexed.spa.fl_str_mv |
Agrosavia Agrovoc |
| dc.relation.references.spa.fl_str_mv |
De Agricultura, Ministerio (Ed.): Metodología para la identificación general de la frontera agrícola en Colombia. Unidad de Planificación Rural Agropecuaria, 2018 Chatterjee, A. ; Sha, J. ; Mukherjee, J. ; Aikat, S. ; Misra, A.: Unsupervised land cover classification of hybrid and dual-polarized images using deep convolutional neural network. En: IEEE Geoscience and Remote Sensing Letters 18 (6) (2021), p. 969–973 Chen, Z. ; Wang, Y. ; Han, W. ; Feng, R. ; Chen, J.: An improved pretraining strategy-based scene classification with deep learning. En: IEEE Geoscience and Remote Sensing Letters 15 (5) (2020), p. 844–848 Cheng, G. ; Xie, X. ; Han, J. ; Guo, L. ; Xia, G.: Remote sensing image scene classification meets deep learning: challenges, methods, benchmarks, and opportunities. En: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13 (2020), p. 3735–3756 Dai, X. ; Wu, X. ; Wang, B. ; Zhang, L.: Semisupervised scene classification for remote sensing images: a method based on convolutional neural networks and ensemble learning. En: IEEE Geoscience and Remote Sensing Letters 16 (6) (2019), p. 869–873 Gao, Y. ; Li, Q.: A segmented particle swarm optimization convolutional neural network for land cover and land use classification of remote sensing images. En: Remote Sensing Letters 10 (12) (2019), p. 1182–1191 González, F. ; Ramos-Pollán, R. ; Gallego-Mejia, J.: Kernel density matrices for probabilistic deep learning. En: arXiv: 2305.18204v2 (2023) Han, Y. ; Liu, Y. ; Hong, Z. ; Zhang, Y. ; Yang, S. ; Wang, J.: Sea ice image classification based on heterogeneous data fusion and deep learning. En: Remote Sensing 13 (4) (2021), p. 1–20 Haykin, S.: Neural Networks and Learning Machines, 3rd edition. Prentice Hall, 2008 He, K. ; Zhang, X. ; Ren, S. ; Sun, J.: Deep residual learning for image recognition. En: arXiv: 1512.03385v1 (2015) Helber, P. ; Bischke, B. ; Dengel, A. ; Borth, D.: Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification. En: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12 (7) (2019), p. 2217–2226 Hong, D. ; Gao, L. ; Yokoya, N. ; Yao, J. ; Chanussot, J. ; Du, Q. ; Zhang, B.: More diverse means better: multimodal deep learning meets remote-sensing imagery classification. En: IEEE Transactions on Geoscience and Remote Sensing 59 (5) (2021), p. 4340–4354 Howard, A. ; Zhu, M. ; Chen, B. ; Kalenichenko, D. ; Wang, W. ; Weyand, T. ; Andreetto, M. ; Adam, H.: MobileNets: Efficient convolutional neural networks for mobile version applications. En: arXiv: 1704.04861v1 (2017) Illarionova, S. ; Trekin, A. ; Ignatiev, V. ; Oseledets, I.: Neural-based hierarchical approach for detailed dominant forest species classification by multispectral satellite imagery. En: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14 (2021), p. 1810–1820 Jamali, A. ; Mahdianpari, M. ; Brisco, B. ; Granger, J. ; Mohammadimanesh, F. ; Salehi, B.: Comparing solo versus ensemble convolutional neural networks for wetland classification using multi-spectral satellite imagery. En: Remote Sensing 13 (11) (2021) Jozdani, S. ; Johnson, B. ; Chen, D.: Comparing deep neural networks, ensemble classifiers, and support vector machine algorithms for object-based urban landuse/land cover classification. En: Remote Sensing 11 (14) (2019) Kussul, N. ; Lavreniuk, M. ; Skakun, S. ; Shelestov, A.: Deep learning classification of land cover and crop types using remote sensing data. En: IEEE Geoscience and Remote Sensing Letters 14 (5) (2017), p. 778–782 De Lima, R. ; Marfurt, K.: Convolutional neural network for remote-sensing scene classification: transfer learning analysis. En: Remote Sensing 12 (1) (2020) Liu, Q. ; Hang, R. ; Song, H. ; Li, Z.: Learning multiscale deep features for highresolution satellite image scene classification. En: IEEE Transactions on Geoscience and Remote Sensing 56 (1) (2018), p. 117–126 Liu, X. ; He, C. ; Zhang, Q. ; Liao, M.: Statistical convolutional neural network for land-cover classification from SAR images. En: IEEE Geoscience and Remote Sensing Letters 17 (9) (2020), p. 1548–1552 Liu, X. ; Zhou, Y. ; Zhao, J. ; Yao, R. ; Liu, B. ; Zheng, Y.: Siamese convolutional neural networks for remote sensing scene classification. En: IEEE Geoscience and Remote Sensing Letters 16 (8) (2019), p. 1200–1204 Liu, Y. ; Zhong, Y. ; Qin, Q.: Scene classification based on multiscale convolutional neural network. En: IEEE Transactions on Geoscience and Remote Sensing 56 (12) (2018), p. 7109–7121 Maggiori, E. ; Tarabalka, Y. ; Charpiat, G. ; Alliez, P.: Convolutional neural networks for large-scale remote-sensing image classification. En: IEEE Transactions on Geoscience and Remote Sensing 55 (2) (2017), p. 645–657 Peng, C. ; Li, Y. ; Jiao, L. ; Shang, R.: Efficient convolutional neural architecture search for remote sensing image scene classification. En: IEEE Transactions on Geoscience and Remote Sensing 59 (7) (2021), p. 6092–6105 Qin, S. ; Guo, X. ; Sun, J. ; Qiao, S. ; Zhang, L. ; Yao, J. ; Cheng, Q. ; Zhang, Y.: Landslide detection from open satellite imagery using distant domain transfer learning. En: Remote Sensing 13 (11) (2021) Rosso, M. D. ; Sebastianelli, A. ; Spiller, D. ; Mathieu, P. ; Ullo, S.: Onboard volcanic eruption detection through CNNs and satellite multispectral imagery. En: Remote Sensing 13 (17) (2021) Scott, G. ; England, M. ; Starms, W. ; Marcum, R. ; Davis, C.: Training deep convolutional neural networks for land-cover classification of high-resolution imagery. En: IEEE Geoscience and Remote Sensing Letters 14 (4) (2017), p. 549–553 Shendryk, Y. ; Rist, Y. ; Ticehurst, C. ; Thorburn, P.: Deep learning for multimodal classification of cloud, shadow and land cover scenes in PlanetScope and Sentinel-2 imagery. En: IEEE Geoscience and Remote Sensing Letters 157 (2019), p. 124–136 Shi, C. ; Lv, Z ; Shen, H. ; Fang, L. ; You, Z.: Improved metric learning with the CNN for very-high-resolution remote sensing image classification. En: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14 (2021), p. 631–644 Siesto, G. ; Fern´andez-Sellers, M. ; Lozano-Tello, A.: Crop classification of satellite imagery using synthetic multitemporal and multispectral images in convolution neural networks. En: Remote Sensing 13 (17) (2021) Simonyan, K. ; Zisserman, A.: Very deep convolutional networks for large-scale image recognition. En: arXiv: 1409.1556v6 (2015) Szegedy, C. ; Vanhoucke, V. ; Ioffe, S. ; Shlens, J.: Rethinking the inception architecture for computer vision. En: arXiv: 1512.00567v3 (2015) Tang, X. ; Ma, Q. ; Zhang, X. ; Liu, F. ; Ma, J. ; Jiao, L.: Attention consistent network for remote sensing scene classification. En: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14 (2021), p. 2030–2045 Tao, C. ; Lu, W. ; Qi, J. ; Wang, H.: Spatial information considered network for scene classification. En: IEEE Geoscience and Remote Sensing Letters 18 (6) (2021), p. 948–988 Tu, B. ; Kuang, W. ; He, W. ; Zhang, G. ; Peng, Y.: Robust learning of mislabeled training samples for remote sensing image scene. En: IEEE Geoscience and Remote Sensing Letters 18 (2) (2021), p. 241–245 Wang, G. ; Fan, B. ; Xiang, S. ; Pan, C.: Aggregating rich hierarchical features for scene classification in remote sensing imagery. En: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10 (9) (2017), p. 4104–4115 Wu, Z. ; Hou, B. ; Jiao, L.: Multiscale CNN with autoencoder regularization joint contextual attention network for SAR image classification. En: IEEE Transactions on Geoscience and Remote Sensing 59 (2) (2021), p. 1200–1213 Xu, X. ; Li, W. ; Ran, Q. ; Du, Q. ; Gao, L. ; Zhang, B.: Multisource remote sensing data classification based on convolutional neural network. En: IEEE Transactions on Geoscience and Remote Sensing 56 (2) (2018), p. 937–949 Yang, N. ; Tang, H. ; Sun, H. ; Yang, X.: Dropband: a simple and effective method for promoting the scene classification accuracy of convolutional neural networks for VHR remote sensing imagery. En: IEEE Geoscience and Remote Sensing Letters 15 (2) (2018), p. 257–261 Yoo, C. ; Han, D. ; Im, J. ; Bechtel, B.: Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using landsat images. En: ISPRS Journal of Photogrammetry and Remote Sensing 157 (2019), p. 155–170 Yu, X. ; Wu, X. ; Luo, C. ; Ren, P.: Deep learning in remote sensing scene classification: a data augmentation enhanced convolutional neural network framework. En: GIScience and Remote Sensing 54 (5) (2017), p. 741–758 Zhang, C. ; Sargent, I. ; Pan, X. ; Li, H. ; Gardiner, A. ; Hare, J. ; Atkinson, P.: An object-based convolutional neural network (OCNN) for urban land use classification. En: Remote Sensing of Environment 216 (2018), p. 57–70 Zhang, C. ; Wang, X. ; Ma, L. ; Lu, X.: Tropical cyclone intensity classification and estimation using infrared satellite images with deep learning. En: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14 (2021), p. 2070–2086 Zhang, Y. ; Zheng, X. ; Yuan, Y. ; Lu, X.: Attribute-cooperated convolutional neural network for remote sensing image classification. En: IEEE Transactions on Geoscience and Remote Sensing 58 (12) (2020), p. 8358–8371 Zhao, Z. ; Li, J. ; Luo, Z. ; Li, J. ; Chen, C.: Remote sensing image scene classification based on an enhanced attention module. En: IEEE Geoscience and Remote Sensing Letters 18 (11) (2021), p. 1926–1930 Zhong, Y. ; Fei, F. ; Liu, Y. ; Zhao, B. ; Jiao, H. ; Zhang, L.: SatCNN: satellite image dataset classification using agile convolutional neural networks. En: Remote Sensing Letters 8 (2) (2017), p. 136–145 Zhou, S. ; Zhou, Y. ; Liu, B.: Using siamese capsule networks for remote sensing scene classification. En: Remote Sensing Letters 11 (8) (2020), p. 757–766 Zhu, Y. ; Geis, C. ; So, E. ; Jin, Y.: Multitemporal relearning with convolutional LSTM models for land use classification. En: IEEE Journal of Selected Topics in Applied Earth Observasions and Remote Sensing 14 (2021), p. 3251–3265 |
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Bogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y Computación |
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Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2González Osorio, Fabio Augusto35912f60905ba6e179208c70e6024e80Ramos Pollán, Raúlfbb946669aa88f49e505179423a7f3cdÁlvarez Montoya, Sebastián Felipec42c1838e5ab2a16f150f01234afb5aeMindlab2024-04-02T00:30:16Z2024-04-02T00:30:16Z2023https://repositorio.unal.edu.co/handle/unal/85836Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramas, fotografías, mapas, planosLas imágenes satelitales son una fuente valiosa de información sobre la tierra, que nos permiten analizar su superficie y las estructuras creadas por el ser humano, como la cobertura del suelo, la vegetación, la topografía y las áreas urbanas. En las últimas décadas, se han producido avances significativos para mejorar la calidad de estas imágenes, incluyendo el uso de imágenes multiespectrales de alta resolución que brindan una descripción más precisa de los objetos y su entorno. Además, se han desarrollado modelos de aprendizaje profundo utilizando estas imágenes para la clasificación y segmentación de objetos; principalmente en ámbitos urbanos y climáticos, con pocos modelos enfocados en la agricultura y los cultivos. Sin embargo, dado que Colombia es un país con una vasta extensión de tierra dedicada a la agricultura, es importante desarrollar modelos de aprendizaje profundo para clasificar y predecir la distribución de estas áreas, lo que brinda información valiosa tanto al gobierno como a los agricultores. En este estudio se utilizaron imágenes de los satélites Sentinel 2 tomadas en el año 2020, que fueron preprocesadas y georreferenciadas. Luego se determinó la cantidad de área porcentual de zonas agrícolas en cada imagen, que es la variable que permite la etiquetación de las mismas, como Frontera agrícola o No en la tarea de clasificación. Se utilizaron redes neuronales convolucionales profundas, incluyendo MobileNet, ResNet50, Inception v3 y VGG 19, con una entrada de resolución de imagen de 100 x 100. De igual manera, se utilizaron modelos con arquitecturas más simples para hacer una comparación adicional entre estos tipos de modelos; los cuales se dividieron como modelos shallow convolutional y modelos basados en Quantum Kernel Mixtures. Donde se observan mejores resultados utilizando estas arquitecturas más simples para esta tarea de clasificación con este tipo de imágenes. En resumen, este estudio demuestra cómo el uso de modelos de aprendizaje profundo junto con imágenes satelitales de alta resolución puede proporcionar información valiosa para la agricultura, permitiendo una mejor comprensión y planificación de las áreas de cultivo en Colombia. (Texto tomado de la fuente).Satellite images constitute a valuable source of information about the Earth, enabling the analysis of its surface and human-created structures, such as land cover, vegetation, topography, and urban areas. Significant advancements have been made in recent decades to enhance the quality of these images, including the utilization of high-resolution multispectral images that provide a more precise description of objects and their surroundings. Additionally, deep learning models have been developed using these images for object classification and segmentation, primarily in urban and climatic contexts, with limited focus on agriculture and crops. Given that Colombia encompasses vast agricultural lands, it is crucial to develop deep learning models for classifying and predicting the distribution of these areas, offering valuable insights to both the government and farmers. This study utilized images from Sentinel 2 satellites captured in the year 2020, which underwent preprocessing and georeferencing. The percentage of agricultural area in each image was then determined, serving as the variable for labeling them as either agricultural land or No in the classification task. Deep convolutional neural networks, including MobileNet, ResNet50, Inception v3, and VGG 19, were employed with an input image resolution of 100 x 100. Similarly, models with simpler architectures were used for additional comparison, categorized as shallow convolutional models and models based on Quantum Kernel Mixtures. Interestingly, superior results were observed using these simpler architectures for this classification task with high-resolution satellite images. In summary, this study demonstrates how the combination of deep learning models and high-resolution satellite images can provide valuable information for agriculture, facilitating a better understanding and planning of cultivation areas in Colombia.MaestríaMagíster en Ingeniería - Ingeniería de Sistemas y ComputaciónComputación aplicada - Sistemas inteligentesx, 71 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y ComputaciónFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá630 - Agricultura y tecnologías relacionadas::633 - Cultivos de campo y de plantación620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingenieríaAprendizaje automáticomachine learningImágenes satelitalesZonas agrícolas en ColombiaAprendizaje por transferenciaRedes neuronales profundasSatellite imagesAgricultural zones in ColombiaTransfer learningDeep neural networksSistema de información geográficaZona ruralGeographical information systemsRural areasClasificación de zonas agrícolas en Colombia por medio de imágenes satelitales con redes neuronales profundasClassification of agricultural areas in Colombia through satellite images with deep neural networksTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMColombiahttp://vocab.getty.edu/page/tgn/1000050AgrosaviaAgrovocDe Agricultura, Ministerio (Ed.): Metodología para la identificación general de la frontera agrícola en Colombia. Unidad de Planificación Rural Agropecuaria, 2018Chatterjee, A. ; Sha, J. ; Mukherjee, J. ; Aikat, S. ; Misra, A.: Unsupervised land cover classification of hybrid and dual-polarized images using deep convolutional neural network. En: IEEE Geoscience and Remote Sensing Letters 18 (6) (2021), p. 969–973Chen, Z. ; Wang, Y. ; Han, W. ; Feng, R. ; Chen, J.: An improved pretraining strategy-based scene classification with deep learning. En: IEEE Geoscience and Remote Sensing Letters 15 (5) (2020), p. 844–848Cheng, G. ; Xie, X. ; Han, J. ; Guo, L. ; Xia, G.: Remote sensing image scene classification meets deep learning: challenges, methods, benchmarks, and opportunities. En: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13 (2020), p. 3735–3756Dai, X. ; Wu, X. ; Wang, B. ; Zhang, L.: Semisupervised scene classification for remote sensing images: a method based on convolutional neural networks and ensemble learning. En: IEEE Geoscience and Remote Sensing Letters 16 (6) (2019), p. 869–873Gao, Y. ; Li, Q.: A segmented particle swarm optimization convolutional neural network for land cover and land use classification of remote sensing images. En: Remote Sensing Letters 10 (12) (2019), p. 1182–1191González, F. ; Ramos-Pollán, R. ; Gallego-Mejia, J.: Kernel density matrices for probabilistic deep learning. En: arXiv: 2305.18204v2 (2023)Han, Y. ; Liu, Y. ; Hong, Z. ; Zhang, Y. ; Yang, S. ; Wang, J.: Sea ice image classification based on heterogeneous data fusion and deep learning. En: Remote Sensing 13 (4) (2021), p. 1–20Haykin, S.: Neural Networks and Learning Machines, 3rd edition. Prentice Hall, 2008He, K. ; Zhang, X. ; Ren, S. ; Sun, J.: Deep residual learning for image recognition. En: arXiv: 1512.03385v1 (2015)Helber, P. ; Bischke, B. ; Dengel, A. ; Borth, D.: Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification. 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En: IEEE Journal of Selected Topics in Applied Earth Observasions and Remote Sensing 14 (2021), p. 3251–3265EstudiantesInvestigadoresMaestrosPúblico generalResponsables políticosLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/85836/3/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD53ORIGINAL1018486698.2024.pdf1018486698.2024.pdfTesis de Maestría en Ingeniería - Ingeniería de Sistemas y Computaciónapplication/pdf9335049https://repositorio.unal.edu.co/bitstream/unal/85836/4/1018486698.2024.pdffdabfb910c46e5fa826f37b3aad75d31MD54THUMBNAIL1018486698.2024.pdf.jpg1018486698.2024.pdf.jpgGenerated Thumbnailimage/jpeg4679https://repositorio.unal.edu.co/bitstream/unal/85836/5/1018486698.2024.pdf.jpg49618e3139f176f5c82dfbc3de877622MD55unal/85836oai:repositorio.unal.edu.co:unal/858362024-04-01 23:04:22.241Repositorio Institucional Universidad Nacional de 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