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Technical Note

Semantic Segmentation of Tree-Canopy in Urban Environment with Pixel-Wise Deep Learning

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Faculty of Engineering, Architecture and Urbanism and Geography, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, Brazil
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Computing Science and Mathematics Division, University of Stirling, Stirling FK9 4LA, UK
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Faculty of Engineering and Architecture and Urbanism, University of Western São Paulo, Rodovia Raposo Tavares, km 572, Bairro Limoeiro 19067-175, Brazil
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Environment and Regional Development Program, University of Western São Paulo, Rodovia Raposo Tavares, km 572, Bairro Limoeiro 19067-175, Brazil
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Environmental Science and Sustainability, INOVISÃO Universidade Católica Dom Bosco, Av. Tamandaré, 6000, Campo Grande 79117-900, Brazil
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Agronomy Program, University of Western São Paulo, Rodovia Raposo Tavares, km 572, Bairro Limoeiro 19067-175, Brazil
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Forest Engineering Department, Santa Catarina State University, Avenida Luiz de Camões 2090, Lages 88520-000, Brazil
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Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil
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Author to whom correspondence should be addressed.
Academic Editor: Bailang Yu
Remote Sens. 2021, 13(16), 3054; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163054
Received: 6 May 2021 / Revised: 2 July 2021 / Accepted: 16 July 2021 / Published: 4 August 2021
Urban forests are an important part of any city, given that they provide several environmental benefits, such as improving urban drainage, climate regulation, public health, biodiversity, and others. However, tree detection in cities is challenging, given the irregular shape, size, occlusion, and complexity of urban areas. With the advance of environmental technologies, deep learning segmentation mapping methods can map urban forests accurately. We applied a region-based CNN object instance segmentation algorithm for the semantic segmentation of tree canopies in urban environments based on aerial RGB imagery. To the best of our knowledge, no study investigated the performance of deep learning-based methods for segmentation tasks inside the Cerrado biome, specifically for urban tree segmentation. Five state-of-the-art architectures were evaluated, namely: Fully Convolutional Network; U-Net; SegNet; Dynamic Dilated Convolution Network and DeepLabV3+. The experimental analysis showed the effectiveness of these methods reporting results such as pixel accuracy of 96,35%, an average accuracy of 91.25%, F1-score of 91.40%, Kappa of 82.80% and IoU of 73.89%. We also determined the inference time needed per area, and the deep learning methods investigated after the training proved to be suitable to solve this task, providing fast and effective solutions with inference time varying from 0.042 to 0.153 minutes per hectare. We conclude that the semantic segmentation of trees inside urban environments is highly achievable with deep neural networks. This information could be of high importance to decision-making and may contribute to the management of urban systems. It should be also important to mention that the dataset used in this work is available on our website. View Full-Text
Keywords: remote sensing; image segmentation; sustainability; convolutional neural network; urban environment remote sensing; image segmentation; sustainability; convolutional neural network; urban environment
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MDPI and ACS Style

Martins, J.A.C.; Nogueira, K.; Osco, L.P.; Gomes, F.D.G.; Furuya, D.E.G.; Gonçalves, W.N.; Sant’Ana, D.A.; Ramos, A.P.M.; Liesenberg, V.; dos Santos, J.A.; de Oliveira, P.T.S.; Junior, J.M. Semantic Segmentation of Tree-Canopy in Urban Environment with Pixel-Wise Deep Learning. Remote Sens. 2021, 13, 3054. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163054

AMA Style

Martins JAC, Nogueira K, Osco LP, Gomes FDG, Furuya DEG, Gonçalves WN, Sant’Ana DA, Ramos APM, Liesenberg V, dos Santos JA, de Oliveira PTS, Junior JM. Semantic Segmentation of Tree-Canopy in Urban Environment with Pixel-Wise Deep Learning. Remote Sensing. 2021; 13(16):3054. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163054

Chicago/Turabian Style

Martins, José A.C., Keiller Nogueira, Lucas P. Osco, Felipe D.G. Gomes, Danielle E.G. Furuya, Wesley N. Gonçalves, Diego A. Sant’Ana, Ana P.M. Ramos, Veraldo Liesenberg, Jefersson A. dos Santos, Paulo T.S. de Oliveira, and José M. Junior 2021. "Semantic Segmentation of Tree-Canopy in Urban Environment with Pixel-Wise Deep Learning" Remote Sensing 13, no. 16: 3054. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163054

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