Streamlining the study of the Tierra del Fuego forest through the use of deep learning
Understanding plant-herbivorous relationships allows to optimize the way to manage and protect natural spaces. In this paper the study of this relationship in the ñire forests (Nothofagus antarctica) of the province of Tierra del Fuego (Argentina) is approached. Using trap cameras to monitor such in...
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ftunivlaplata:oai:sedici.unlp.edu.ar:10915/91024 2023-05-15T13:49:49+02:00 Streamlining the study of the Tierra del Fuego forest through the use of deep learning Viera, Leonel González, Federico Soler, Rosina Romano, Lucas Feierherd, Guillermo Eugenio 2019-10 application/pdf 438-445 http://sedici.unlp.edu.ar/handle/10915/91024 en eng XXV Congreso Argentino de Ciencias de la Computación (CACIC) (Universidad Nacional de Río Cuarto, Córdoba, 14 al 18 de octubre de 2019) http://sedici.unlp.edu.ar/handle/10915/90359 http://sedici.unlp.edu.ar/handle/10915/91024 isbn:978-987-688-377-1 http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) CC-BY-NC-SA Ciencias Informáticas Objeto de conferencia 2019 ftunivlaplata 2020-03-22T01:01:35Z Understanding plant-herbivorous relationships allows to optimize the way to manage and protect natural spaces. In this paper the study of this relationship in the ñire forests (Nothofagus antarctica) of the province of Tierra del Fuego (Argentina) is approached. Using trap cameras to monitor such interaction offers the opportunity to quickly collect large amounts of data. However, to take advantage of its potential, a large investment in trained personnel to analyze and filter the images of interest is required. The present work seeks to establish a path to significantly reduce this obstacle using the advances of machine and deep learning in the recognition of objects from images. XVII Workshop Computación Gráfica, Imágenes y Visualización. Red de Universidades con Carreras en Informática Conference Object Antarc* Antarctica Tierra del Fuego Universidad Nacional de La Plata (UNLP): SeDiCI (Servicio de Difusión de la Creación Intelectual) Argentina |
institution |
Open Polar |
collection |
Universidad Nacional de La Plata (UNLP): SeDiCI (Servicio de Difusión de la Creación Intelectual) |
op_collection_id |
ftunivlaplata |
language |
English |
topic |
Ciencias Informáticas |
spellingShingle |
Ciencias Informáticas Viera, Leonel González, Federico Soler, Rosina Romano, Lucas Feierherd, Guillermo Eugenio Streamlining the study of the Tierra del Fuego forest through the use of deep learning |
topic_facet |
Ciencias Informáticas |
description |
Understanding plant-herbivorous relationships allows to optimize the way to manage and protect natural spaces. In this paper the study of this relationship in the ñire forests (Nothofagus antarctica) of the province of Tierra del Fuego (Argentina) is approached. Using trap cameras to monitor such interaction offers the opportunity to quickly collect large amounts of data. However, to take advantage of its potential, a large investment in trained personnel to analyze and filter the images of interest is required. The present work seeks to establish a path to significantly reduce this obstacle using the advances of machine and deep learning in the recognition of objects from images. XVII Workshop Computación Gráfica, Imágenes y Visualización. Red de Universidades con Carreras en Informática |
format |
Conference Object |
author |
Viera, Leonel González, Federico Soler, Rosina Romano, Lucas Feierherd, Guillermo Eugenio |
author_facet |
Viera, Leonel González, Federico Soler, Rosina Romano, Lucas Feierherd, Guillermo Eugenio |
author_sort |
Viera, Leonel |
title |
Streamlining the study of the Tierra del Fuego forest through the use of deep learning |
title_short |
Streamlining the study of the Tierra del Fuego forest through the use of deep learning |
title_full |
Streamlining the study of the Tierra del Fuego forest through the use of deep learning |
title_fullStr |
Streamlining the study of the Tierra del Fuego forest through the use of deep learning |
title_full_unstemmed |
Streamlining the study of the Tierra del Fuego forest through the use of deep learning |
title_sort |
streamlining the study of the tierra del fuego forest through the use of deep learning |
publishDate |
2019 |
url |
http://sedici.unlp.edu.ar/handle/10915/91024 |
geographic |
Argentina |
geographic_facet |
Argentina |
genre |
Antarc* Antarctica Tierra del Fuego |
genre_facet |
Antarc* Antarctica Tierra del Fuego |
op_relation |
XXV Congreso Argentino de Ciencias de la Computación (CACIC) (Universidad Nacional de Río Cuarto, Córdoba, 14 al 18 de octubre de 2019) http://sedici.unlp.edu.ar/handle/10915/90359 http://sedici.unlp.edu.ar/handle/10915/91024 isbn:978-987-688-377-1 |
op_rights |
http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
op_rightsnorm |
CC-BY-NC-SA |
_version_ |
1766252337828986880 |