7 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 biodiversity mapping, particularly in boreal ecosystems where tree species like aspen (Popu…
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…
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…
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…
3D forest semantic segmentation using multispectral LiDAR and 3D deep learning
Narges Takhtkeshha, Lauris Bocaux, Lassi Ruoppa +4
Conservation and decision-making regarding forest resources necessitate regular forest inventory. Light detection and ranging (LiDAR) in laser scanning systems has gained significa…