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Fire Scars Mapping Over Brazilian Amazon Forest by Exploiting Sentinel-2 Data and Deep Learning
| dc.contributor.author | Camacho-De Angulo, Yineth Viviana | |
| dc.contributor.author | Rosa, Nicolas Cechinel | |
| dc.contributor.author | Solano-Correa, Yady Tatiana | |
| dc.contributor.author | Roisenberg, Mauro | |
| dc.date.accessioned | 2024-09-12T14:01:44Z | |
| dc.date.available | 2024-09-12T14:01:44Z | |
| dc.date.issued | 2024-07-12 | |
| dc.date.submitted | 2024-09-11 | |
| dc.identifier.citation | Y. V. Camacho-De Angulo; N. C. Rosa; Y. T. Solano-Correa; M. Roisenberg, "Fire Scars Mapping Over Brazilian Amazon Forest by Exploiting Sentinel-2 Data and Deep Learning," in 2024 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Athens, Greece, Jul. 2024. DOI: 10.1109/IGARSS53475.2024.10642369. | spa |
| dc.identifier.uri | https://hdl.handle.net/20.500.12585/12732 | |
| dc.description.abstract | Wildfires in the Brazilian Amazon have raised significant concerns owing to the environmental, social, and global impacts associated with these events. They have led to habitat loss for various species and release of substantial amounts of carbon dioxide into the atmosphere. Thereby contributing to climate change and deterioration of air quality due to pollutants emission. The integration of advanced technologies, including high-spatial resolution satellite data and image processing algorithms, enables a more precise and comprehensive understanding of the wildfire scenario. This research introduces a model based on deep learning that can be applied over Sentinel-2 images to reliably detect fire scars with an accuracy above 90% (92% on training data and 82% on validation data). A SpectrumNet convolutional neural network was employed, incorporating features extracted from spectral bands at 10m and 20m. | spa |
| dc.format.extent | 4 páginas | |
| dc.format.mimetype | application/pdf | spa |
| dc.language.iso | eng | spa |
| dc.source | IEEE International Geoscience and Remote Sensing Symposium (IGARSS) | spa |
| dc.title | Fire Scars Mapping Over Brazilian Amazon Forest by Exploiting Sentinel-2 Data and Deep Learning | spa |
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| datacite.rights | http://purl.org/coar/access_right/c_14cb | spa |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | spa |
| dc.type.driver | info:eu-repo/semantics/lecture | spa |
| dc.type.hasversion | info:eu-repo/semantics/publishedVersion | spa |
| dc.identifier.doi | 10.1109/IGARSS53475.2024.10642369 | |
| dc.subject.keywords | Deep Learning | spa |
| dc.subject.keywords | Remote Sensing | spa |
| dc.subject.keywords | Semantic Segmentation | spa |
| dc.subject.keywords | Wildfires | spa |
| dc.subject.keywords | Brazilian Amazon | spa |
| dc.rights.accessrights | info:eu-repo/semantics/closedAccess | spa |
| dc.identifier.instname | Universidad Tecnológica de Bolívar | spa |
| dc.identifier.reponame | Repositorio Universidad Tecnológica de Bolívar | spa |
| dc.publisher.place | Cartagena de Indias | spa |
| dc.subject.armarc | LEMB | |
| dc.publisher.faculty | Ciencias Básicas | spa |
| dc.type.spa | http://purl.org/coar/resource_type/c_c94f | spa |
| dc.audience | Investigadores | spa |
| oaire.resourcetype | http://purl.org/coar/resource_type/c_c94f | spa |
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Universidad Tecnológica de Bolívar - 2017 Institución de Educación Superior sujeta a inspección y vigilancia por el Ministerio de Educación Nacional. Resolución No 961 del 26 de octubre de 1970 a través de la cual la Gobernación de Bolívar otorga la Personería Jurídica a la Universidad Tecnológica de Bolívar.
