Deep Learning para Classificação Supervisionada de Imagens CBERS-4A da Área Urbana de Rio Claro (SP)
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O presente artigo tem por objetivo avaliar a acurácia do mapeamento do uso e cobertura da terra, realizado em um trecho da área urbana de Rio Claro (SP), a partir de técnicas de Deep Learning e utilizando uma imagem CBERS-4A (WPM) com 2 m de resolução espacial. Foi estruturada uma rede neural convolucional U-Net a partir de script em Python, utilizando as bibliotecas Keras e Tensor Flow. A verdade terrestre, utilizada para treinamento e verificação da acurácia do modelo, foi elaborada por meio de classificação supervisionada da mesma imagem no software ArcGIS Pro, utilizando o algoritmo Support Vector Machine (SVM) e procedimentos de pós-classificação, incluindo aplicação de filtro majoritário e edição manual de pixels. O resultado obtido pela U-Net foi comparado ao resultado obtido pelo SVM (sem pós-classificação), visando compreender se há ganhos de acurácia, tendo em vista o maior esforço humano, para a criação do ground truth, e computacional, para processamento dos dados, inerente às técnicas de Deep Learning. Para isso, ambos os resultados foram submetidos a avaliação de acurácia utilizando as métricas Overall Accuracy, Precision, Recall, F1 Score e Kappa. Constatou-se que o modelo U-Net apresenta melhor acurácia em todas elas, destacando-se o aumento da Precision de 0,48 (SVM) para 0,78 (U-Net). Tais resultados indicam o potencial das técnicas de Deep Learning para o mapeamento do uso e cobertura da terra em áreas urbanas a partir de imagens de alta resolução, o que pode contribuir, de modo significativo, para ações de planejamento e gestão territorial nos municípios brasileiros
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