# Multispectral LiDAR data for extracting tree points in urban and suburban areas

## TL;DR

This paper demonstrates that multispectral LiDAR combined with deep learning models significantly improves the accuracy and efficiency of extracting urban and suburban tree points, aiding in environmental monitoring and infrastructure management.

## Contribution

It introduces the use of multispectral LiDAR data with advanced deep learning models for more accurate tree point extraction in complex urban environments.

## Key findings

- Superpoint Transformer (SPT) achieved 85.28% mIoU.
- Incorporating pNDVI reduced error rate by 10.61 percentage points.
- SPT demonstrated notable time efficiency and high accuracy.

## Abstract

Monitoring urban tree dynamics is vital for supporting greening policies and reducing risks to electrical infrastructure. Airborne laser scanning has advanced large-scale tree management, but challenges remain due to complex urban environments and tree variability. Multispectral (MS) light detection and ranging (LiDAR) improves this by capturing both 3D spatial and spectral data, enabling detailed mapping. This study explores tree point extraction using MS-LiDAR and deep learning (DL) models. Three state-of-the-art models are evaluated: Superpoint Transformer (SPT), Point Transformer V3 (PTv3), and Point Transformer V1 (PTv1). Results show the notable time efficiency and accuracy of SPT, with a mean intersection over union (mIoU) of 85.28%. The highest detection accuracy is achieved by incorporating pseudo normalized difference vegetation index (pNDVI) with spatial data, reducing error rate by 10.61 percentage points (pp) compared to using spatial information alone. These findings highlight the potential of MS-LiDAR and DL to improve tree extraction and further tree inventories.