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

2025-08-27 · Narges Takhtkeshha, Gabriele Mazzacca, Fabio Remondino, Juha Hyyppä, Gottfried Mandlburger arxiv

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.

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Benchmarking individual tree segmentation using multispectral airborne laser scanning data: the FGI-EMIT dataset

2025-11-01 · Lassi Ruoppa, Tarmo Hietala, Verneri Seppänen, Josef Taher 외 arxiv

Individual tree segmentation (ITS) from LiDAR point clouds is fundamental for applications such as forest inventory, carbon monitoring and biodiversity assessment. Traditionally, ITS has been achieved with unsupervised g…

Point Clouds

3D LULC classification using multispectral LiDAR and deep learning: current and prospective schemes

2026-05-21 · Narges Takhtkeshha, Aldino Rizaldy, Markus Hollaus, Juha Hyyppä 외 arxiv

Land Use Land Cover (LULC) classification is essential for national 3D mapping, geospatial analysis, and sustainable planning. Multispectral (MS) LiDAR provides synchronized spatial-spectral information, and deep learnin…

Semantic Segmentation

Multispectral airborne laser scanning dataset for tree species classification: MS-ALS-SPECIES

2026-04-27 · Matti Hyyppä, Klaara Salolahti, Eric Hyyppä, Xiaowei Yu 외 arxiv

The shift from stand-level to individual-tree-level forest assessments supports improved biodiversity mapping, particularly in boreal ecosystems where tree species like aspen (Populus tremula L.) play a keystone role. Wh…

Point Clouds

Joint Learning from Earth Observation and OpenStreetMap Data to Get Faster Better Semantic Maps

2017-05-17 · Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre

In this work, we investigate the use of OpenStreetMap data for semantic labeling of Earth Observation images. Deep neural networks have been used in the past for remote sensing data classification from various sensors, i…

Earth Observation

OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression

2020-05-14 · CVPR 2020 6 · Lila Huang, Shenlong Wang, Kelvin Wong, Jerry Liu 외

We present a novel deep compression algorithm to reduce the memory footprint of LiDAR point clouds. Our method exploits the sparsity and structural redundancy between points to reduce the bitrate. Towards this goal, we f…

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