A deep learning-based mobile application for tree species mapping in RGB images

被引:13
作者
Carvalho, Mario de Araujo [1 ]
Marcato, Jose [2 ]
Correa Martins, Jose Augusto [2 ]
Zamboni, Pedro [2 ]
Costa, Celso Soares [3 ]
Siqueira, Henrique Lopes [2 ]
Araujo, Marcio Santos [2 ]
Goncalves, Diogo Nunes [1 ]
Garcia Furuya, Danielle Elis [6 ]
Osco, Lucas Prado [6 ]
Marques Ramos, Ana Paula [6 ]
Li, Jonathan [4 ,5 ]
de Castro, Amaury Antonio [1 ]
Goncalves, Wesley Nunes [1 ,2 ]
机构
[1] Univ Fed Mato Grosso do Sul, Fac Comp Sci, Av Costa & Silva, BR-79070900 Campo Grande, MS, Brazil
[2] Univ Fed Mato Grosso do Sul, Fac Engn Architecture & Urbanism & Geog, BR-79070900 Campo Grande, MS, Brazil
[3] Fed Inst Educ Sci & Technol Mato Grosso do Sul, BR-79909000 Ponta Pora, MS, Brazil
[4] Univ Waterloo, Dept Geog & Environm Management, Waterloo, ON N2L 3G1, Canada
[5] Univ Waterloo, Dept Syst Design Engn, Waterloo, ON N2L 3G1, Canada
[6] Univ Western Sao Paulo, Program Environm & Reg Dev, BR-19067175 Presidente Prudente, SP, Brazil
关键词
Convolutional neural networks; Mobile devices; Trees mapping; Remote sensing; CLASSIFICATION; SERVICES;
D O I
10.1016/j.jag.2022.103045
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
Tree species mapping is an important type of information demanded in different study fields. However, this task can be expensive and time-consuming, making it difficult to monitor extensive areas. Hence, automatic methods are required to optimize tree species mapping. Here, we propose a deep learning-based mobile application tool for tree species classification in high-spatial-resolution RGB images. Several deep learning architectures were evaluated, including mobile networks and traditional models. A total of 2,349 images were used, of which 1,174 images consisted of the Dipteryx alata species and 1,175 images of other local species. These images were manually annotated and randomly divided into training (70%), validation (20%), and testing (10%) subsets, considering the five-fold cross-validation. We evaluated the accuracy and speed (GPU and CPU) of all the implemented deep learning architectures. We found out that the traditional networks have the best performance in terms of F1 score; however, mobile networks are faster. Inception V3 model achieved the best accuracy (F1 score of 97.4%), and MobileNet the worst (F1 score of 83.84%). The MobileNet obtained the best classification speed for CPU (with a mean execution time of 102.8 ms) and GPU (72.4 ms) units. For comparison, Inception V3 achieved a mean execution time of 1058.3 ms for CPU and 634.5 ms for GPU. We conclude that the mobile application proposed can be successfully used to run mobile networks and traditional networks for image classification, but the balance between accuracy and execution time needs to be carefully assessed. This mobile app is a tool for researchers, policymakers, non-governmental organizations, and the general public who intends to assess the tree species, providing a GUI-based platform for non-programmers to access the capabilities of deep learning models in complex classification tasks.
引用
收藏
页数:9
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