@Article{DinizCPSFBAS:2021:LaDeAp,
               author = "Diniz, Cesar and Cortinhas, Luiz and Pinheiro, Maria Luize and 
                         Sadeck, Lu{\'{\i}}s and Fernandes Filho, Alexandre and Baumann, 
                         Luis R. F. and Adami, Marcos and Souza Filho, Pedro Walfir M.",
          affiliation = "{Solved—Solutions in Geoinformation} and {Solved—Solutions in 
                         Geoinformation} and {Solved—Solutions in Geoinformation} and 
                         {Solved—Solutions in Geoinformation} and {Solved—Solutions in 
                         Geoinformation} and {Universidade Federal de Goi{\'a}s (UFG)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade Federal do Par{\'a} (UFPA)}",
                title = "A large-scale deep-learning approach for multi-temporal aqua and 
                         salt-culture mapping",
              journal = "Remote Sensing",
                 year = "2021",
               volume = "13",
               number = "8",
                pages = "e1415",
                month = "Apr.",
             keywords = "aquaculture, salt-culture, U-Net, Tensor-Flow, Google Earth 
                         Engine, Landsat.",
             abstract = "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 
                         pixels 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.",
                  doi = "10.3390/rs13081415",
                  url = "http://dx.doi.org/10.3390/rs13081415",
                 issn = "2072-4292",
             language = "en",
           targetfile = "remotesensing-13-01415.pdf",
        urlaccessdate = "20 maio 2024"
}