Spatio-Temporal Deep Learning Approach to Map Deforestation in Amazon Rainforest

Maretto, Raian Vargas; Fonseca, Leila María García; Jacobs, Nathan; Körting, Thales Sehn; Bendini, Hugo do Nascimento; Parente, Leandro

doi:10.1109/lgrs.2020.2986407

Cited by 44 publications

(29 citation statements)

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“…USED BY [9], [10] The methodology presented in this work was also evaluated in the regions evaluated by [17], [18]. Two datasets with 2000 mosaic images each were assembled using the images evaluated by [17], [18], respectively. Table 12 shows the images used for the construction of each dataset and the division of the set for training and validation.…”

Section: Evaluation Of Methodology In Regionsmentioning

confidence: 99%

“…In [18], the authors used CNN to perform the classification of deforested areas in a region of southern Pará state. The authors used Landsat-8/OLI images and PRODES data as the gold standard.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The papers presented in this review showed that machine learning techniques have great potential to classify remote sensing images and to perform automatic segmentation in the most varied types of vegetation cover in the Amazon region. In [9], [10], [18], the authors used deep learning techniques to classify deforestation regions in the Amazon. The results achieved were quite satisfactory.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Although PRODES and TerraClass are large projects and provide very reliable data, they still rely on a significant portion of work done by human intervention. In order to speed up and make the process more efficient, several studies have been done using machine learning tools [7]- [18].…”

Section: Introductionmentioning

confidence: 99%

“…The works of [9], [10], [18] used deep learning to detect deforested areas in regions of the Brazilian Amazon. The results obtained by these authors were quite satisfactory.…”

Section: Introductionmentioning

confidence: 99%

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Methodology of Data Fusion Using Deep Learning for Semantic Segmentation of Land Types in the Amazon

Oliveira¹,

Costa

Filho

2020

IEEE Access

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This study proposes a methodology using deep learning and a multi-resolution segmentation algorithm to perform the semantic segmentation of remote sensing images. Initially the image is segmented using a CNN, and then an image with homogeneous regions is generated using a multi-resolution segmentation algorithm. Finally, a data fusion process is performed with these two images, generating the final classified image. The field of study was the Brazilian Amazon region. The proposed methodology classifies images in the following classes: forest, pasture and agriculture. The input data used were LANDSAT-8/OLI images. The reference data were extracted from the results of the TerraClass project in 2014. Two datasets were evaluated: the first with six bands and the second with three bands. Three CNN architectures were evaluated together with three optimization methods: SGDM, ADAM, and RMSProp and the dropout and L2 regularization methods as methods for generalization improvement. The best model, CNN + optimization method + technique for generalization improvement, evaluated in the validation set, was submitted to a 5-fold cross validation methodology, and the results were compared with pre-trained networks using the learning transfer methodology; in this case the networks used for comparison were ResNet50, InceptionResnetv2, MobileNetv2 and Xception. The proposed methodology was evaluated through image segmentation of some regions of the Amazon. Finally, the proposed methodology was evaluated in regions used by other authors. The accuracy values obtained for the images evaluated were over 99%. INDEX TERMS deep learning; convolutional neural networks; remote sensing; image segmentation.

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Section: Evaluation Of Methodology In Regionsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The works of [9], [10], [18] used deep learning to detect deforested areas in regions of the Brazilian Amazon. The results obtained by these authors were quite satisfactory.…”

Section: Introductionmentioning

confidence: 99%

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