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@MastersThesis{Miranda:2023:PiClLa,
               author = "Miranda, Mateus de Souza",
                title = "AI4LUC: pixel-based classification of land use and land cover via 
                         deep learning and a Cerrado image dataset",
               school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                 year = "2023",
              address = "S{\~a}o Jos{\'e} dos Campos",
                month = "2023-03-28",
             keywords = "pixel-based image classification, deep learning, Cerrado, 
                         CBERS-4A, classifica{\c{c}}{\~a}o de imagem baseada em pixel, 
                         aprendizado profundo, Cerrado, CBERS-4A.",
             abstract = "The Cerrado biome is known for the biodiversity of flora, as well 
                         as for its potential in agricultural production. Its landscapes of 
                         land use and land cover (LULC) are monitored in order to analyze 
                         and understand the social, economic, and environmental aspects 
                         related to causative factors and impacts of these activities. 
                         There are many efforts by the Remote Sensing (RS) community for 
                         employing machine learning (ML) or deep learning (DL) techniques 
                         aiming to improve classification tasks, in terms of either 
                         pixel-based classification or contextual classification. However, 
                         a few datasets containing images with high spatial resolution, 
                         representativeness, and a huge number of samples about the Cerrado 
                         biome are available. For supervised learning of either DL or ML 
                         models, dataset samples must be labeled. This procedure currently 
                         relies on manual execution, demanding significant time and 
                         attention. For instance, it involves generating and labeling 
                         reference masks, where specific pixels indicate the class to which 
                         they belong in the segment. Driven by these motivations, this 
                         masters dissertation strives to make a valuable contribution to 
                         the field of pixel-based classification, specifically focusing on 
                         semantic segmentation of Land Use and Land Cover (LULC) using deep 
                         learning techniques applied to a dataset of satellite images from 
                         the Cerrado region. To achieve this objective, a novel approach 
                         named Artificial Intelligence for Land Use and Land Cover 
                         Classification (AI4LUC) is introduced. Thus, a dataset regarding 
                         the Cerrado biome was created, called CerraData, amounting to 
                         unlabeled 2.5 million patches with a height and width of 256 
                         pixels, and two meters of spatial resolution. The spectral bands 
                         were obtained from the Wide Panchromatic and Multispectral Camera 
                         (WPM) of the China-Brazil Earth Resources-4A (CBERS-4A) satellite. 
                         From this dataset, two novel labeled versions were designed. 
                         Furthermore, a novel convolutional neural network (CNN) called 
                         CerraNetv3 has been developed to enhance the pixel-based 
                         classification task. CerraNetv3, along with Google DeepLabv3plus, 
                         collaboratively contributes to this endeavor. Additionally, an 
                         innovative technique has been introduced to automate the 
                         generation and labeling of reference masks. By leveraging the 
                         capabilities of CerraNetv3, these reference masks are utilized to 
                         facilitate the training process of DeepLabv3plus for pixel-based 
                         classification. AI4LUC was subjected to a comparative analysis 
                         with other related approaches in the domain of semantic 
                         segmentation and contextual classification to assess its 
                         viability. The findings revealed that CerraNetv3 achieved the 
                         highest performance in the contextual classification experiment, 
                         attaining an impressive F1-score of 0.9289. As for the automatic 
                         mask generation and labeling method, it yielded an overall score 
                         of 0.6738, with F1-score metrics. In contrast, DeepLabv3plus 
                         obtained significantly lower scores of 0.2805 for the same metric. 
                         The lower scores of the mask generation method can be attributed 
                         to occasional deficiencies in the quality of generated masks, 
                         resulting in mislabeling by the CerraNetv3 classifier. 
                         Consequently, DeepLabv3plus also exhibited suboptimal performance. 
                         RESUMO: O bioma Cerrado {\'e} conhecido pela biodiversidade da 
                         flora, bem como pelo seu potencial na produ{\c{c}}{\~a}o 
                         agr{\'{\i}}cola. Suas paisagens de uso e cobertura da terra 
                         (LULC) s{\~a}o monitoradas a fim de analisar e compreender os 
                         aspectos sociais, econ{\^o}micos e ambientais relacionados aos 
                         fatores causadores e impactos dessas atividades. Existem muitos 
                         esfor{\c{c}}os da comunidade de Sensoriamento Remoto (SR) para 
                         empregar t{\'e}cnicas de aprendizado de m{\'a}quina (AM) ou 
                         aprendizado profundo (AP) com o objetivo de melhorar as tarefas de 
                         classifica{\c{c}}{\~a}o, seja em termos de 
                         classifica{\c{c}}{\~a}o baseada em pixels ou 
                         classifica{\c{c}}{\~a}o contextual. No entanto, poucos conjuntos 
                         de dados contendo imagens com alta resolu{\c{c}}{\~a}o espacial, 
                         representatividade e um grande n{\'u}mero de amostras sobre o 
                         bioma Cerrado est{\~a}o dispon{\'{\i}}veis. Para aprendizado 
                         supervisionado de modelos AP ou AM, as amostras de conjunto de 
                         dados devem ser rotuladas. Este procedimento atualmente depende de 
                         execu{\c{c}}{\~a}o manual, exigindo muito tempo e 
                         aten{\c{c}}{\~a}o. Por exemplo, a gera{\c{c}}{\~a}o e 
                         rotulagem de m{\'a}scaras de refer{\^e}ncia, onde cada pixel 
                         indicam a classe a que pertencem no segmento. Impulsionada por 
                         essas motiva{\c{c}}{\~o}es, esta disserta{\c{c}}{\~a}o de 
                         mestrado visa contribuir para o campo da classifica{\c{c}}{\~a}o 
                         baseada em pixels, focando especificamente na 
                         segmenta{\c{c}}{\~a}o sem{\^a}ntica do uso e cobertura da Terra 
                         (LULC) usando t{\'e}cnicas de AP aplicadas a um conjunto de dados 
                         de imagens de sat{\'e}lite do Cerrado. Para alcan{\c{c}}ar este 
                         objetivo, uma nova metodologia, denominada Artificial Intelligence 
                         for Land Use and Land Cover Classification (AI4LUC), {\'e} 
                         apresentada. Assim, foi criado um conjunto de dados referente ao 
                         bioma Cerrado, denominado CerraData, totalizando 2,5 milh{\~o}es 
                         de manchas n{\~a}o rotuladas com altura e largura de 256 pixels e 
                         dois metros de resolu{\c{c}}{\~a}o espacial. As bandas 
                         espectrais foram obtidas da Wide Panchromatic and Multispectral 
                         Camera (WPM) do sat{\'e}lite CBERS-4A. A partir deste conjunto de 
                         dados, duas novas vers{\~o}es rotuladas foram projetadas. 
                         Al{\'e}m disso, uma nova rede neural convolucional (CNN) chamada 
                         CerraNetv3 foi desenvolvida para tarefa de 
                         classifica{\c{c}}{\~a}o contextual. Esta rede foi introduzida a 
                         no m{\'e}todo para automatizar a gera{\c{c}}{\~a}o e rotulagem 
                         de m{\'a}scaras de refer{\^e}ncia, as quais s{\~a}o utilizadas 
                         para o treinamento do DeepLabv3plus. AI4LUC foi submetido a uma 
                         an{\'a}lise comparativa com outras abordagens no dom{\'{\i}}nio 
                         da segmenta{\c{c}}{\~a}o sem{\^a}ntica e 
                         classifica{\c{c}}{\~a}o contextual para avaliar a sua 
                         viabilidade. Os resultados revelaram que o CerraNetv3 
                         alcan{\c{c}}ou o melhor desempenho no experimento de 
                         classifica{\c{c}}{\~a}o contextual, atingindo de 0,9289 com 
                         F1-score. Quanto {\`a} gera{\c{c}}{\~a}o autom{\'a}tica de 
                         m{\'a}scara e ao m{\'e}todo de rotulagem, obteve uma 
                         pontua{\c{c}}{\~a}o geral de 0,6738, com F1-score. As 
                         pontua{\c{c}}{\~o}es mais baixas desse m{\'e}todo podem ser 
                         associadas a qualidade das m{\'a}scaras geradas, resultando em 
                         rotulagem incorreta pelo classificador CerraNetv3. 
                         Consequentemente, o DeepLabv3plus obteve 0,2805, desempenho abaixo 
                         do ideal esperado.",
            committee = "Shiguemori, {\'E}lcio Hideiti (presidente) and Santiago 
                         J{\'u}nior, Valdivino Alexandre de (orientador) and K{\"o}rting, 
                         Thales Sehn (orientador) and Escada, Maria Isabel Sobral and Papa, 
                         Jo{\~a}o Paulo",
         englishtitle = "AI4LUC: classifica{\c{c}}{\~a}o baseada em pixels do uso e 
                         cobertura da terra considerando um conjunto de imagens do 
                         Cerrado",
             language = "en",
                pages = "77",
                  ibi = "8JMKD3MGP3W34T/48QQB65",
                  url = "http://urlib.net/ibi/8JMKD3MGP3W34T/48QQB65",
           targetfile = "publicacao.pdf",
        urlaccessdate = "23 maio 2024"
}


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