CODES AND DATASETS

ForestFormer3D (2025): A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds
Multiopt_VoI_SI (2024): Multi-objective value of information assessment using stochastic programming: addressing uncertainty in site index determination
Towards Enhancing Field-Based Vegetation Monitoring (2024): A Deep Learning Approach for Species Identification and Coverage Estimation from Ground-level Imagery [Data set]
VegCover (2024): Species Identification and Coverage Estimation from Ground-level Imagery for Vegetation Monitoring
BranchPoseNet (2024): Characterizing tree branching with a deep learning-based pose estimation approach
ForAINET (2024): Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning
NIBIO_MLS (2024): a forest point cloud panoptic segmentation dataset from mobile laser scanning (Geoslam Horizon)
FOR-species20K dataset (2024): FOR-species20K dataset, for benchmarking tree species classification from proximally-sensed laser scanning data.
FOR-instance (2023): FOR-instance: a UAV laser scanning benchmark dataset for semantic and instance segmentation of individual trees.
Point2tree (2023): Instance and semantic segmentation of dense laser scanning point clouds from terrestrial platforms (TLS/MLS).
taperNOR (2023): Taper models for spruce, pine and birch in Norway and helper functions.
SmartForest_UAV_damage_detection (2022): Trained a YOLOv5 object-detection model for forest snow damage detection.
FOR-species20K (2022): Sensor-agnostic tree species classification using proximal laser scanning (TLS, MLS, ULS) and CNNs
optBuck (2022): An R package for handling single-grip forest harvester data and bucking optimization
YOLOv5-whorlDetector (2022): This repo contains the R scripts to detect whorls from dense drone laser scanning point clouds.
wheelRuts_semanticSegmentation (2022): This repo includes the scripts to replicate the methods developed in Bhatnagar et al. (2022) to perform a semantic segmentation of wheel-ruts caused by forestry machinery based on drone RGB imagery.

CODES AND DATASETS

ForestFormer3D (2025): A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds

Multiopt_VoI_SI (2024): Multi-objective value of information assessment using stochastic programming: addressing uncertainty in site index determination

Towards Enhancing Field-Based Vegetation Monitoring (2024): A Deep Learning Approach for Species Identification and Coverage Estimation from Ground-level Imagery [Data set]

VegCover (2024): Species Identification and Coverage Estimation from Ground-level Imagery for Vegetation Monitoring

BranchPoseNet (2024): Characterizing tree branching with a deep learning-based pose estimation approach

ForAINET (2024): Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning

NIBIO_MLS (2024): a forest point cloud panoptic segmentation dataset from mobile laser scanning (Geoslam Horizon)

FOR-species20K dataset (2024): FOR-species20K dataset, for benchmarking tree species classification from proximally-sensed laser scanning data.

FOR-instance (2023): FOR-instance: a UAV laser scanning benchmark dataset for semantic and instance segmentation of individual trees.

Point2tree (2023): Instance and semantic segmentation of dense laser scanning point clouds from terrestrial platforms (TLS/MLS).

taperNOR (2023): Taper models for spruce, pine and birch in Norway and helper functions.

SmartForest_UAV_damage_detection (2022): Trained a YOLOv5 object-detection model for forest snow damage detection.

FOR-species20K (2022): Sensor-agnostic tree species classification using proximal laser scanning (TLS, MLS, ULS) and CNNs

optBuck (2022): An R package for handling single-grip forest harvester data and bucking optimization

YOLOv5-whorlDetector (2022): This repo contains the R scripts to detect whorls from dense drone laser scanning point clouds.

wheelRuts_semanticSegmentation (2022): This repo includes the scripts to replicate the methods developed in Bhatnagar et al. (2022) to perform a semantic segmentation of wheel-ruts caused by forestry machinery based on drone RGB imagery.