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Article

A Large-Scale Deep-Learning Approach for Multi-Temporal Aqua and Salt-Culture Mapping

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Solved—Solutions in Geoinformation, Belém 66075-750, Brazil
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Geoscience Institute, UFPA—Federal University of Pará, Belém 66075-110, Brazil
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Technology Institute, UFPA—Federal University of Pará, Belém 66075-110, Brazil
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Philosophy and Human Sciences Institute, UFPA—Federal University of Pará, Belém 66075-110, Brazil
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Institute of Agricultural Sciences, UFRA—Federal Rural University of the Amazon, Belém 66077-813, Brazil
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Institute of Mathematics and Statistics, UFG—Federal University of Goias, Goiania 74690-900, Brazil
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INPE—National Institute for Space Research, Amazon Spatial Coordination, São Paulo 12227-010, Brazil
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ITV—Instituto Tecnológico Vale, Belém 66055-090, Brazil
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Author to whom correspondence should be addressed.
Academic Editor: Konstantinos Topouzelis
Remote Sens. 2021, 13(8), 1415; https://doi.org/10.3390/rs13081415
Received: 25 February 2021 / Revised: 31 March 2021 / Accepted: 31 March 2021 / Published: 7 April 2021
(This article belongs to the Special Issue State-of-the-Art Remote Sensing in South America)
Aquaculture and salt-culture are relevant economic activities in the Brazilian Coastal Zone (BCZ). However, automatic discrimination of such activities from other water-related covers/uses is not an easy task. In this sense, convolutional neural networks (CNN) have the advantage of predicting a given pixel’s class label by providing as input a local region (named patches or chips) around that pixel. Both the convolutional nature and the semantic segmentation capability provide the U-Net classifier with the ability to access the “context domain” instead of solely isolated pixel values. Backed by the context domain, the results obtained show that the BCZ aquaculture/saline ponds occupied ~356 km2 in 1985 and ~544 km2 in 2019, reflecting an area expansion of ~51%, a rise of 1.5× in 34 years. From 1997 to 2015, the aqua-salt-culture area grew by a factor of ~1.7, jumping from 349 km2 to 583 km2, a 67% increase. In 2019, the Northeast sector concentrated 93% of the coastal aquaculture/salt-culture surface, while the Southeast and South sectors contained 6% and 1%, respectively. Interestingly, despite presenting extensive coastal zones and suitable conditions for developing different aqua-salt-culture products, the North coast shows no relevant aqua or salt-culture infrastructure sign. View Full-Text
Keywords: aquaculture; salt-culture; U-Net; Tensor-Flow; Google Earth Engine; Landsat aquaculture; salt-culture; U-Net; Tensor-Flow; Google Earth Engine; Landsat
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MDPI and ACS Style

Diniz, C.; Cortinhas, L.; Pinheiro, M.L.; Sadeck, L.; Fernandes Filho, A.; Baumann, L.R.F.; Adami, M.; Souza-Filho, P.W.M. A Large-Scale Deep-Learning Approach for Multi-Temporal Aqua and Salt-Culture Mapping. Remote Sens. 2021, 13, 1415. https://doi.org/10.3390/rs13081415

AMA Style

Diniz C, Cortinhas L, Pinheiro ML, Sadeck L, Fernandes Filho A, Baumann LRF, Adami M, Souza-Filho PWM. A Large-Scale Deep-Learning Approach for Multi-Temporal Aqua and Salt-Culture Mapping. Remote Sensing. 2021; 13(8):1415. https://doi.org/10.3390/rs13081415

Chicago/Turabian Style

Diniz, Cesar, Luiz Cortinhas, Maria Luize Pinheiro, Luís Sadeck, Alexandre Fernandes Filho, Luis R. F. Baumann, Marcos Adami, and Pedro Walfir M. Souza-Filho. 2021. "A Large-Scale Deep-Learning Approach for Multi-Temporal Aqua and Salt-Culture Mapping" Remote Sensing 13, no. 8: 1415. https://doi.org/10.3390/rs13081415

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