Grant number: | 25/05405-6 |
Support Opportunities: | Scholarships in Brazil - Doctorate |
Start date: | September 01, 2025 |
End date: | March 31, 2029 |
Field of knowledge: | Physical Sciences and Mathematics - Geosciences - Geodesy |
Principal Investigator: | Mauricio Galo |
Grantee: | Matheus Ferreira da Silva |
Host Institution: | Faculdade de Ciências e Tecnologia (FCT). Universidade Estadual Paulista (UNESP). Campus de Presidente Prudente. Presidente Prudente , SP, Brazil |
Abstract Mapping urban forests using remote sensing can provide indicators to promote sustainability and enhance the quality of life in cities. The imbalance between built-up areas and urban vegetation can increase the risk of respiratory diseases, contribute to the development of adverse microclimates, and lead to additional issues that raise public concern and increase costs. In Brazil, most information on urban trees is gathered through manual forest inventories, which limits data collection and makes it costly due to the need for large-scale campaigns to cover entire cities. Therefore, this project aims to develop automated methods for extracting information from multimodal point clouds, i.e., those obtained from different sensors, such as LiDAR (Light Detection and Ranging) and digital cameras, and acquired from multiple platforms, including both aerial and terrestrial sources. The goal is to optimize the management of urban forests and monitor the interaction between trees and infrastructure, including power lines, buildings, and pavements. Overall, this approach has three potential contributions: i) the use of multimodal three-dimensional data to support urban forest mapping in Brazil, with an emphasis on publicly available data collected in municipalities; ii) the development of a deep learning (DL) based approach for processing point clouds; iii) the analysis of topological relationships using adjacency graphs to identify conflicts between vegetation and urban infrastructure. (AU) | |
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