@InProceedings{MartinsZagl:2019:ApCoNe,
author = "Martins, Felipe Ferraz and Zaglia, Matheus Cavassan",
affiliation = "{Pontif{\'{\i}}cia Universidade Cat{\'o}lica do Rio de Janeiro
(PUC-Rio)} and {Instituto Nacional de Pesquisas Espaciais
(INPE)}",
title = "Application of convolutional neural network to pixel-wise
classification in deforestation detection using PRODES data",
booktitle = "Anais... do 20º Simp{\'o}sio Brasileiro de Geoinform{\'a}tica",
year = "2019",
editor = "Lisboa Filho, Jugurta and Monteiro, Antonio Miguel Vieira",
organization = "Simp{\'o}sio Brasileiro de Geoinform{\'a}tica, 20. (GEOINFO)",
publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
address = "S{\~a}o Jos{\'e} dos Campos",
keywords = "geoinformatica.",
abstract = "The INPE PRODES project, which since the 1980s maps and quantifies
deforestation in the Brazilian Legal Amazon, can be considered the
main systematic monitoring project for tropical forests in the
world. Over the time, the project has gone through several stages,
and today its methodology is the visual interpretation of images
by remote sensing experts. This paper aims to evaluate the use of
neural networks to automate this process, improving accuracy and
minimizing the time required for interpretation. Results will be
compared to official PRODES data.",
conference-location = "S{\~a}o Jos{\'e} dos Campos",
conference-year = "11 -13 nov. 2019",
issn = "2179-4847",
language = "pt",
organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
ibi = "8JMKD3MGPDW34R/3UFDE98",
url = "http://urlib.net/ibi/8JMKD3MGPDW34R/3UFDE98",
targetfile = "57-65.pdf",
urlaccessdate = "21 maio 2024"
}