Evaluation of semantic segmentation methods for deforestation detection in the amazon

verfasst von

R. B. Andrade, G. A. O. P. Costa, G. L. A. Mota, M. X. Ortega, R. Q. Feitosa, P. J. Soto, C. Heipke

Abstract

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.

Organisationseinheit(en)

Institut für Photogrammetrie und Geoinformation

Externe Organisation(en)

Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio)
Rio de Janeiro State University

Typ

Konferenzaufsatz in Fachzeitschrift

Journal

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

Band

43

Seiten

1497-1505

Anzahl der Seiten

9

ISSN

1682-1750

Publikationsdatum

22.08.2020

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Information systems, Geografie, Planung und Entwicklung

Ziele für nachhaltige Entwicklung

SDG 13 – Klimaschutzmaßnahmen

Elektronische Version(en)

https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1497-2020 (Zugang: Offen)