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20202026
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cs.CV2026

A Framework for Individual Tree Growth Reconstruction Using Multi-Platform Laser Scanning

Daniella Tavi, Valtteri Soininen, Lassi Ruoppa +2

Accurate tree-level forest monitoring using laser scanning data requires reliable tree delineation, consistent tree correspondence across multitemporal point clouds, and accurate e…

cs.CV2026

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

Narges Takhtkeshha, Aldino Rizaldy, Markus Hollaus +3

Land Use Land Cover (LULC) classification is essential for national 3D mapping, geospatial analysis, and sustainable planning. Multispectral (MS) LiDAR provides synchronized spatia…

cs.CV2026

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

Matti Hyyppä, Klaara Salolahti, Eric Hyyppä +9

The shift from stand-level to individual-tree-level forest assessments supports improved species mapping and biodiversity monitoring, particularly in boreal ecosystems where tree s…

cs.CV2025

NormalView: tree species classification from backpack and aerial lidar data using geometric projections

Juho Korkeala, Jesse Muhojoki, Josef Taher +4

Laser scanning has proven to be an invaluable tool in assessing the decomposition of forest environments. Mobile laser scanning (MLS) has shown to be highly promising for extremely…

cs.CV2020

Multimodal End-to-End Learning for Autonomous Steering in Adverse Road and Weather Conditions

Jyri Maanpää, Josef Taher, Petri Manninen +3

Autonomous driving is challenging in adverse road and weather conditions in which there might not be lane lines, the road might be covered in snow and the visibility might be poor.…

cs.CV2020

CAE-LO: LiDAR Odometry Leveraging Fully Unsupervised Convolutional Auto-Encoder for Interest Point Detection and Feature Description

Deyu Yin, Qian Zhang, Jingbin Liu +6

As an important technology in 3D mapping, autonomous driving, and robot navigation, LiDAR odometry is still a challenging task. Appropriate data structure and unsupervised deep lea…