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.
|Number of pages||5|
|Journal||IEEE Geoscience and Remote Sensing Letters|
|State||Published - May 2021|
Bibliographical noteFunding Information:
Manuscript received November 5, 2019; revised February 3, 2020; accepted February 13, 2020. Date of publication April 28, 2020; date of current version April 22, 2021. This work was developed as part of the project “Development of systems to prevent forest fires and monitor vegetation cover in the Brazilian Cerrado”, with financial support of the Forest Investment Program (World Bank Project #P143185) and in part by the National Science Foundation under Grant IIS-1553116. (Corresponding author: Raian V. Maretto.) Raian V. Maretto, Leila M. G. Fonseca, Thales S. Körting, and Hugo N. Bendini are with the National Institute for Space Research (INPE), São José dos Campos 12227-010, Brazil (e-mail: firstname.lastname@example.org).
© 2004-2012 IEEE.
- Convolutional neural networks (CNNs)
- deep learning (DL)
- spatio-temporal analysis
ASJC Scopus subject areas
- Geotechnical Engineering and Engineering Geology
- Electrical and Electronic Engineering