Development of a Low-Cost Terrestrial Mobile Mapping System for Urban Vegetation Detection Using Convolutional Neural Networks

Autores

DOI:

https://doi.org/10.11137/1982-3908_2022_45_46008

Palavras-chave:

Mobile geospatial data acquisition systems, NIR Imaging, Semantic segmentation

Resumo

Urbanization brought a lot of pollution-related issues that are mitigable by the presence of urban vegetation. Therefore, it is necessary to map vegetation in urban areas, to assist the planning and implementation of public policies. As a technology presented in the last decades, the so-called Terrestrial Mobile Mapping Systems - TMMS, are capable of providing cost and time effective data acquisition, they are composed primarily by a Navigation System and an Imaging System, both mounted on a rigid platform, attachable to the top of
a ground vehicle. In this context, it is proposed the creation of a low-cost TMMS, which has the feature of imaging in the near-infrared (NIR) where the vegetation is highly discriminable. After the image acquisition step, it becomes necessary for the semantic segmentation of vegetation and non-vegetation. The current state of the art algorithms in semantic segmentation scope are the Convolutional Neural Networks - CNNs. In this study, CNNs were trained and tested, reaching a mean value of 83% for the Intersection Over Union (IoU) indicator. From the results obtained, which demonstrated good performance for the trained neural network, it is possible to conclude
that the developed TMMS is suitable to capture data regarding urban vegetation.

Biografia do Autor

Kauê de Moraes Vestena, Federal University of Paraná

PhD candidate in Geodetic Sciences (2021-Present) .Master degree in Geodetic Sciences at UFPR (2017-2020). Graduated in Cartographic and Surveying Engineering by UFPR (2012-2017). Technician in Surveying at UTFPR (2009-2012). http://lattes.cnpq.br/9787699103652534

Daniel Rodrigues dos Santos, Instituto Militar de Engenharia

Seção de Engenharia Cartográfica

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2022-05-18

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Ciências do Ambiente