17 citations · 44 across the 10 of their papers we have counts for
11 papers
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…
Riverine Land Cover Mapping through Semantic Segmentation of Multispectral Point Clouds
Sopitta Thurachen, Josef Taher, Matti Lehtomäki +6
Accurate land cover mapping in riverine environments is essential for effective river management, ecological understanding, and geomorphic change monitoring. This study explores th…
Learning Image-based Tree Crown Segmentation from Enhanced Lidar-based Pseudo-labels
Julius Pesonen, Stefan Rua, Josef Taher +3
Mapping individual tree crowns is essential for tasks such as maintaining urban tree inventories and monitoring forest health, which help us understand and care for our environment…
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…
Benchmarking individual tree segmentation using multispectral airborne laser scanning data: the FGI-EMIT dataset
Lassi Ruoppa, Tarmo Hietala, Verneri Seppänen +6
Individual tree segmentation (ITS) from LiDAR point clouds is fundamental for applications such as forest inventory, carbon monitoring and biodiversity assessment. Traditionally, I…
Multispectral airborne laser scanning for tree species classification: a benchmark of machine learning and deep learning algorithms
Josef Taher, Eric Hyyppä, Matti Hyyppä +46
Climate-smart and biodiversity-preserving forestry demands precise information on forest resources, extending to the individual tree level. Multispectral airborne laser scanning (A…
Unsupervised deep learning for semantic segmentation of multispectral LiDAR forest point clouds
Lassi Ruoppa, Oona Oinonen, Josef Taher +5
Point clouds captured with laser scanning systems from forest environments can be utilized in a wide variety of applications within forestry and plant ecology, such as the estimati…
Unsupervised semantic segmentation of urban high-density multispectral point clouds
Oona Oinonen, Lassi Ruoppa, Josef Taher +7
The availability of highly accurate urban airborne laser scanning (ALS) data will increase rapidly in the future, especially as acquisition costs decrease, for example through the…
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.…