@Article{MarettoFoJaKöBePa:2021:SpDeLe,
author = "Maretto, Raian Vargas and Fonseca, Leila Maria Garcia and Jacobs,
Nathan and K{\"o}rting, Thales Sehn and Bendini, Hugo do
Nascimento and Parente, Leandro L.",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)}",
title = "Spatio-Temporal Deep Learning Approach to Map Deforestation in
Amazon Rainforest",
journal = "IEEE Geoscience and Remote Sensing Letters",
year = "2021",
volume = "18",
number = "5",
pages = "771--775",
month = "May",
keywords = "Task analysis, Forestry, Remote sensing, Training, Artificial
satellites, Earth, Semantics, Convolutional neural networks
(CNNs), deep learning (DL), deforestation, spatio-temporal
analysis, U-Net.",
abstract = "We address the task of mapping deforested areas in the Brazilian
Amazon. Accurate maps are an important tool for informing
effective deforestation containment policies. The main existing
approaches to this task are largely manual, requiring significant
effort by trained experts. To reduce this effort, we propose a
fully automatic approach based on spatio-temporal deep
convolutional neural networks. We introduce several
domain-specific components, including approaches for: image
preprocessing; handling image noise, such as clouds and shadow;
and constructing the training data set. We show that our
preprocessing protocol reduces the impact of noise in the training
data set. Furthermore, we propose two spatio-temporal variations
of the U-Net architecture, which make it possible to incorporate
both spatial and temporal contexts. Using a large, real-world data
set, we show that our method outperforms a traditional U-Net
architecture, thus achieving approximately 95% accuracy.",
doi = "10.1109/LGRS.2020.2986407",
url = "http://dx.doi.org/10.1109/LGRS.2020.2986407",
issn = "1545-598X",
language = "en",
targetfile = "maretto_spatio.pdf",
urlaccessdate = "03 jun. 2024"
}