Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/33143
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Semantic segmentation of tree-canopy in urban environment with pixel-wise deep learning
Author(s): Martins, Jose Augusto Correa
Nogueira, Keiller
Osco, Lucas Prado
Gomes, Felipe David Georges
Furuya, Danielle Elis Garcia
Gonçalves, Wesley Nunes
Sant’ana, Diego Andre
Ramos, Ana Paula Marques
Liesenberg, Veraldo
dos Santos, Jefersson Alex
de Oliveira, Paulo Tarso Sanches
Marcato Junior, Jose
Keywords: remote sensing
image segmentation
sustainability
convolutional neural network
urban environment
Issue Date: Aug-2021
Date Deposited: 24-Aug-2021
Citation: 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 & Marcato Junior J (2021) Semantic segmentation of tree-canopy in urban environment with pixel-wise deep learning. Remote Sensing, 13 (16), Art. No.: 3054. https://doi.org/10.3390/rs13163054
Abstract: 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.
DOI Link: 10.3390/rs13163054
Rights: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Licence URL(s): http://creativecommons.org/licenses/by/4.0/

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