@Article{SanchesFMALSPVMHCO:2020:FiReLE,
               author = "Sanches, Ieda Del'Arco and Feitosa, R. Q. and Montibeller, Bruno 
                         and Achanccaray Diaz, P. M. and Luiz, Alfredo J. B. and Soares, M. 
                         D. and Prudente, Victor Hugo Rohden and Vieira, Denis Corte and 
                         Maurano, Lu{\'{\i}}s Eduardo Pinheiro and Happ, Patrick N. and 
                         Chamorro, J. and Oldoni, Lucas Volochen",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Pontif{\'{\i}}cia Universidade Cat{\'o}lica do Rio de Janeiro 
                         (PUC-Rio)} and {University of Tartu} and {Pontif{\'{\i}}cia 
                         Universidade Cat{\'o}lica do Rio de Janeiro (PUC-Rio)} and 
                         {Empresa Brasileira de Pesquisa Agropecu{\'a}ria (EMBRAPA)} and 
                         {Pontif{\'{\i}}cia Universidade Cat{\'o}lica do Rio de Janeiro 
                         (PUC-Rio)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} 
                         and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Pontif{\'{\i}}cia Universidade Cat{\'o}lica do Rio de Janeiro 
                         (PUC-Rio)} and {Pontif{\'{\i}}cia Universidade Cat{\'o}lica do 
                         Rio de Janeiro (PUC-Rio)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "First results of the LEM benchmark database for agricultural 
                         applications",
              journal = "International Archives of the Photogrammetry and Remote Sensing",
                 year = "2020",
               volume = "43",
               number = "B5",
                pages = "251--256",
                month = "Aug.",
                 note = "24th ISPRS Congress - Technical Commission V (TC-V) on Education 
                         and Outreach - Youth Forum; Nice, Virtual; France; 31 Aug. - 2 
                         Sep.",
             keywords = "Optical Images, SAR Images, Tropical area, Crop Recognition, 
                         Random Forest, Fully Convolutional Recurrent Networks.",
             abstract = "Applying remote sensing technology to map and monitor agriculture 
                         and its impacts can greatly contribute for the proper development 
                         of this activity, promoting efficient food, fiber and energy 
                         production. For that, not only remote sensing images are needed, 
                         but also ground truth information, which is a key factor for the 
                         development and improvement of methodologies using remote sensing 
                         data. While a variety of images are current available, inclusive 
                         cost-free images, field reference data is scarcer. For 
                         agricultural applications, especially in tropical regions such as 
                         Brazil, where the agriculture is very dynamic and diverse (recent 
                         agricultural frontiers, crop rotations, multiple cropping systems, 
                         several management practices, etc.), and cultivated over a vast 
                         territory, this task is not trivial. One way of boosting the 
                         researches in agricultural remote sensing is to stimulate people 
                         to share their data, and to foster different groups to use the 
                         same dataset, so distinct methods can be properly compared. In 
                         this context, our group created the LEM Benchmark Database (a 
                         project funded by the ISPRS Scientific Initiative project - 2017) 
                         from the Luiz Eduardo Magalh{\~a}es (LEM) municipality, Bahia 
                         State, Brazil. The database contains a set of pre-processed 
                         multitemporal satellite images (Landsat-8/OLI, Sentinel-2/MSI and 
                         SAR band-C Sentinel-1) and shapefiles of agricultural fields with 
                         their correspondent monthly land use classes, covering the period 
                         of one Brazilian crop year (2017-2018). In this paper we present 
                         the first results obtained with this database.",
                  doi = "10.5194/isprs-archives-XLIII-B5-2020-251-2020",
                  url = "http://dx.doi.org/10.5194/isprs-archives-XLIII-B5-2020-251-2020",
                 issn = "0256-1840",
             language = "en",
           targetfile = "sanches_first.pdf",
        urlaccessdate = "28 abr. 2024"
}