Evaluation of semantic segmentation methods for deforestation detection in the amazon

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Andrade, R.B.; Costa, G.A.O.P.; Mota, G.L.A.; Ortega, M.X.; Feitosa, R.Q. et al.: Evaluation of semantic segmentation methods for deforestation detection in the amazon. In: Paparoditis, N. et al. (Eds.): XXIV ISPRS Congress, Commission III : edition 2020. Katlenburg-Lindau : Copernicus Publications, 2020. (ISPRS Archives ; 43,B3), S. 1497-1505. DOI: https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1497-2020

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Deforestation is a wide-reaching problem, responsible for serious environmental issues, such as biodiversity loss and global climate change. Containing approximately ten percent of all biomass on the planet and home to one tenth of the known species, the Amazon biome has faced important deforestation pressure in the last decades. Devising efficient deforestation detection methods is, therefore, key to combat illegal deforestation and to aid in the conception of public policies directed to promote sustainable development in the Amazon. In this work, we implement and evaluate a deforestation detection approach which is based on a Fully Convolutional, Deep Learning (DL) model: the DeepLabv3+. We compare the results obtained with the devised approach to those obtained with previously proposed DL-based methods (Early Fusion and Siamese Convolutional Network) using Landsat OLI-8 images acquired at different dates, covering a region of the Amazon forest. In order to evaluate the sensitivity of the methods to the amount of training data, we also evaluate them using varying training sample set sizes. The results show that all tested variants of the proposed method significantly outperform the other DL-based methods in terms of overall accuracy and F1-score. The gains in performance were even more substantial when limited amounts of samples were used in training the evaluated methods. © 2020 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives.
License of this version: CC BY 4.0 Unported
Document Type: BookPart
Publishing status: publishedVersion
Issue Date: 2020
Appears in Collections:Fakultät für Bauingenieurwesen und Geodäsie

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pos. country downloads
total perc.
1 image of flag of United States United States 72 18.85%
2 image of flag of India India 43 11.26%
3 image of flag of Brazil Brazil 36 9.42%
4 image of flag of Germany Germany 25 6.54%
5 image of flag of United Kingdom United Kingdom 24 6.28%
6 image of flag of China China 21 5.50%
7 image of flag of France France 13 3.40%
8 image of flag of No geo information available No geo information available 12 3.14%
9 image of flag of Australia Australia 10 2.62%
10 image of flag of Philippines Philippines 9 2.36%
    other countries 117 30.63%

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