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

Martins, José Augusto Correa ; Nogueira, Keiller ; Osco, Lucas Prado ; Gomes, Felipe David Georges ; Furuya, Danielle Elis Garcia ; Gonçalves, Wesley Nunes ; Sant’Ana, Diego André ; Ramos, Ana Paula Marques ; Liesenberg, Veraldo ; dos Santos, Jefersson Alex ; de Oliveira, Paulo Tarso Sanches ; Junior, José Marcato

Remote sensing (Basel, Switzerland), 2021-08, Vol.13 (16), p.3054 [Periódico revisado por pares]

Basel: MDPI AG

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Título:
Semantic Segmentation of Tree-Canopy in Urban Environment with Pixel-Wise Deep Learning
Autor: Martins, José Augusto Correa ; Nogueira, Keiller ; Osco, Lucas Prado ; Gomes, Felipe David Georges ; Furuya, Danielle Elis Garcia ; Gonçalves, Wesley Nunes ; Sant’Ana, Diego André ; Ramos, Ana Paula Marques ; Liesenberg, Veraldo ; dos Santos, Jefersson Alex ; de Oliveira, Paulo Tarso Sanches ; Junior, José Marcato
Assuntos: Algorithms ; Artificial intelligence ; Artificial neural networks ; Automation ; Biodiversity ; Canopies ; Cities ; Climate change ; convolutional neural network ; Datasets ; Decision making ; Deep learning ; Fluid mechanics ; Forests ; Image segmentation ; Inference ; Machine learning ; Neural networks ; Occlusion ; Pixels ; Public health ; Quality of life ; Remote sensing ; Semantic segmentation ; Semantics ; State-of-the-art reviews ; sustainability ; Trees ; Unmanned aerial vehicles ; Urban areas ; Urban drainage ; urban environment ; Urban environments ; Vegetation ; Websites
É parte de: Remote sensing (Basel, Switzerland), 2021-08, Vol.13 (16), p.3054
Descrição: 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.
Editor: Basel: MDPI AG
Idioma: Inglês

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

Basel: MDPI AG

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