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Urban Roadside Crown Identification via Intensity-Calibrated Mobile Laser Scanning Point Cloud

Xu Liu1, Fa Zhu2, Osama Alfarraj3, Fahad Alblehai3,*, Shakir Khan4
1 College of Intelligent Manufacturing, Huanghuai University, Zhumadian, China
2 College of Information Science and Technology & College of Artificial Intelligence, Nanjing Forestry University, Nanjing, China
3 Department of Computer Science and Engineering, College of Applied Studies, King Saud University, Riyadh, Saudi Arabia
4 Information Technology Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
* Corresponding Author: Fahad Alblehai. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.079640

Received 25 January 2026; Accepted 20 July 2026; Published online 02 September 2026

Abstract

Mobile laser scanning (MLS) plays a crucial role in urban vegetation monitoring by enabling the precise identification of roadside trees. However, in cluttered environments where tree crowns intermingle with various objects, methods relying solely on raw intensity data face substantial accuracy limitations. To address this issue, a dedicated calibration method is proposed, which models the relationship between acquisition geometry and intensity values through polynomial fitting. Specifically, calibration models were first established for a given LiDAR sensor using reference data from a standard diffuse reflection panel. A customized MLS platform equipped with this sensor was then deployed to acquire point clouds along urban roadways. After preprocessing, locally planar point clouds were retained for intensity calibration. The calibrated intensity values were subsequently used for tree crown identification. Experiments on an 80 m road segment show that the calibrated intensity improves the F1 score from 0.63 to 0.83, a 32% increase, demonstrating its effectiveness in real-world scenes.

Keywords

Remote sensing; mobile laser scanning; crown recognition; intensity calibration; urban greenery monitoring
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