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EVALUATION OF SEMANTIC SEGMENTATION METHODS FOR DEFORESTATION DETECTION IN THE AMAZON
- Source :
- The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLIII-B3-2020, Pp 1497-1505 (2020), XXIV ISPRS Congress, Commission III : edition 2020, ISPRS Archives ; 43,B3
- Publication Year :
- 2020
- Publisher :
- Copernicus Publications, 2020.
-
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. © 2020 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives.
- Subjects :
- Dewey Decimal Classification::500 | Naturwissenschaften::550 | Geowissenschaften
lcsh:Applied optics. Photonics
Computer science
Biome
Sample (statistics)
lcsh:Technology
Amazon forest
Deforestation
DeepLabv3+
ddc:550
deforestation
change detection
Konferenzschrift
Sustainable development
Amazon rainforest
business.industry
lcsh:T
Deep learning
Global warming
Environmental resource management
deep learning
lcsh:TA1501-1820
semantic segmentation
lcsh:TA1-2040
Artificial intelligence
business
lcsh:Engineering (General). Civil engineering (General)
Change detection
Subjects
Details
- Language :
- English
- ISSN :
- 21949034 and 16821750
- Database :
- OpenAIRE
- Journal :
- The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
- Accession number :
- edsair.doi.dedup.....f3f4557576dceb5d7d4478d4742d7ee6