Urban Tree Species Identification Based on Crown RGB Point Clouds Using Random Forest and PointNet
[EN] The management and identification of forest species in a city are essential tasks for current administrations, particularly in planning urban green spaces. However, the cost and time required are typically high. This study evaluates the potential of RGB point clouds captured by unnamed aerial v...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/230578 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/230578 |
| Access Level: | acceso abierto |
| Palabra clave: | Point cloud PointNet Random forest RGB Tree identification UAV 11.- Conseguir que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles 13.- Tomar medidas urgentes para combatir el cambio climático y sus efectos 15.- Proteger, restaurar y promover la utilización sostenible de los ecosistemas terrestres, gestionar de manera sostenible los bosques, combatir la desertificación y detener y revertir la degradación de la tierra, y frenar la pérdida de diversidad biológica |
| Sumario: | [EN] The management and identification of forest species in a city are essential tasks for current administrations, particularly in planning urban green spaces. However, the cost and time required are typically high. This study evaluates the potential of RGB point clouds captured by unnamed aerial vehicles (UAVs) for automating tree species classification. A dataset of 809 trees (crowns) for eight species was analyzed using a random forest classifier and deep learning with PointNet and PointNet++. In the first case, eleven variables such as the normalized red¿blue difference index (NRBDI), intensity, brightness (BI), Green Leaf Index (GLI), points density (normalized), and height (maximum and percentiles 10, 50, and 90), produced the highest reliability values, with an overall accuracy of 0.70 and a Kappa index of 0.65. In the second case, the PointNet model had an overall accuracy of 0.62, and 0.64 with PointNet++; using the features Z, red, green, blue, NRBDI, intensity, and BI. Likewise, there was a high accuracy in the identification of the species Populus alba L., and Melaleuca armillaris (Sol. ex Gaertn.) Sm. This work contributes to a cost-effective workflow for urban tree monitoring using UAV data, comparing classical machine learning with deep learning approaches and analyzing the trade-offs. |
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