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Found 7 papers, 2 papers with code
SegmentAnyTree: A sensor and platform agnostic deep learning model for tree segmentation using laser scanning data
In conclusion, this study shows the feasibility of a sensor-agnostic model for diverse lidar data, surpassing sensor-specific approaches and setting new standards in tree segmentation, particularly in complex forests.
Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning
Detailed forest inventories are critical for sustainable and flexible management of forest resources, to conserve various ecosystem services.
FOR-instance: a UAV laser scanning benchmark dataset for semantic and instance segmentation of individual trees
In conclusion, the FOR-instance dataset contributes to filling a gap in the 3D forest research, enhancing the development and benchmarking of segmentation algorithms for dense airborne laser scanning data.
Towards accurate instance segmentation in large-scale LiDAR point clouds
Panoptic segmentation is the combination of semantic and instance segmentation: assign the points in a 3D point cloud to semantic categories and partition them into distinct object instances.
Point2Tree(P2T) -- framework for parameter tuning of semantic and instance segmentation used with mobile laser scanning data in coniferous forest
We trained a model based on Pointnet++ architecture which achieves 0. 92 F1-score in semantic segmentation.
Prediction of butt rot volume in Norway spruce forest stands using harvester, remotely sensed and environmental data
We found that forest attributes characterizing the maturity of forest, such as remote sensing-based height, harvested timber volume and quadratic mean diameter at breast height, were among the most important predictor variables.
National mapping and estimation of forest area by dominant tree species using Sentinel-2 data
However, even for municipalities with a decent number of NFI plots, direct NFI estimates were sometimes more precise than MA estimates.
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