21 citations · 40 across the 8 of their papers we have counts for
8 papers
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…
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…
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…
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…
Towards High-Definition Maps: a Framework Leveraging Semantic Segmentation to Improve NDT Map Compression and Descriptivity
Petri Manninen, Heikki Hyyti, Ville Kyrki +3
High-Definition (HD) maps are needed for robust navigation of autonomous vehicles, limited by the on-board storage capacity. To solve this, we propose a novel framework, Environmen…
Leveraging Road Area Semantic Segmentation with Auxiliary Steering Task
Jyri Maanpää, Iaroslav Melekhov, Josef Taher +2
Robustness of different pattern recognition methods is one of the key challenges in autonomous driving, especially when driving in the high variety of road environments and weather…
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.…
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…