dfichuk/tree-species-vancouver-island

Vancouver Island Tree Species LiDAR Dataset

Dataset Description

4,040 labeled tree crowns extracted from airborne LiDAR point clouds for classifying tree species native to Vancouver Island and the Pacific Northwest. Each sample includes 25 engineered LiDAR features (height percentiles, intensity statistics, canopy structure metrics) plus a raw point cloud patch (.npy file), a species label, quality confidence scores, and train/val/test split assignment.

Species

The dataset contains 4,040 samples across 74 taxonIDs (76 unique scientificName entries), with the following dominant species:

taxonID	Scientific Name	Common Name	Count
PSMEM	Pseudotsuga menziesii var. menziesii	Douglas-fir	1,639
TSHE	Tsuga heterophylla	Western hemlock	541
PIPA2	Pinus palustris	Longleaf pine	295
ABAM	Abies amabilis	Pacific silver fir	195
QURU	Quercus rubra	Northern red oak	166
ACRU	Acer rubrum	Red maple	138
QUAL	Quercus alba	White oak	103
RUSP	Rubus spectabilis	Salmonberry	81
ALRU2	Alnus rubra	Red alder	70
QULA2	Quercus laevis	Turkey oak	69
THPL	Thuja plicata	Western redcedar	57
RUAR9	Rubus armeniacus	Himalayan blackberry	53
QUCO2	Quercus coccinea	Scarlet oak	52
NYSY	Nyssa sylvatica	Black tupelo	45
ACCI	Acer circinatum	Vine maple	43
AMLA	Amelanchier laevis	Allegheny serviceberry	43
VAPA	Vaccinium parvifolium	Red huckleberry	42
TABR2	Taxus brevifolia	Pacific yew	28

Plus 56 more species (see unified_metadata.json for the full list).

Data Sources

The dataset is compiled from three sources:

NEON WREF/ABBY (2,949 samples)

• Sites: Wind River Experimental Forest (WREF) and Abby Road (ABBY), Washington, USA
• Ecosystem: Pacific Northwest conifer forests
• Data: NEON DP1.10098.001 Vegetation Structure (labels) + DP1.30003.001 discrete-return LiDAR (2023 RELEASE-2026)
• Split: Train/validation
• Citation: National Ecological Observatory Network. https://www.neonscience.org/data

IDTReeS Competition (1,055 samples)

• Sites: Mountain Lake Biological Station (MLBS, VA) and Ordway-Swisher Biological Station (OSBS, FL), USA
• Ecosystem: Eastern deciduous and southeastern mixed forest
• Data: Individual Tree Crowns polygons from CHM, with species labels
• Split: Train/validation
• Citation: Weinstein, B. G., et al. (2022). Individual Tree Crowns and Species from NEON (IDTReeS). https://doi.org/10.5281/zenodo.5553697

BC Vancouver Island Local (36 samples)

• Location: Vancouver Island, British Columbia, Canada
• Ecosystem: Coastal temperate rainforest
• Data: GPS-located tree stems with field crew species labels; BC government airborne LiDAR (~5 pts/m²)
• Split: Test (holdout — geographic generalization test)
• Note: These field-labeled samples from Vancouver Island serve as a true geographic holdout.

Data Format

Tabular data (CSV files)

unified_dataset.csv contains 4,040 samples with 44 columns:

Column	Description
sample_id	Unique sample identifier
source	Data source: neon, idtrees, or bc_local
taxonID	Standardized 4-6 letter species code (USDA Plants)
scientificName	Full scientific name
species	Common name (grouped; only in unified_dataset.csv)
species_raw	Raw species label from source (only in unified_dataset.csv)
pnw_native	Whether native to Pacific Northwest (boolean)
x_utm10, y_utm10	UTM zone 10N coordinates (m)
patch_file	Relative path to point cloud patch (.npy)
n_points	Total LiDAR point count
n_veg	Vegetation return count
n_ground	Ground return count
ground_elevation	Ground elevation (m)
height_max	Maximum vegetation height (m)
height_mean	Mean vegetation height (m)
height_std	Height standard deviation
height_skew	Height distribution skewness
height_p10/25/50/75/90/95/99	Height percentiles (m)
intensity_mean	Mean return intensity
intensity_std	Intensity standard deviation
intensity_p25/50/75/95	Intensity percentiles
pct_single_return	Percentage of single returns
pct_ground_return	Percentage of ground returns
density_understory	Point density below 2 m
density_mid_canopy	Point density 2-15 m
density_upper_canopy	Point density above 15 m
label_quality_score	Label quality confidence (0-1)
measurement_quality_score	Measurement quality confidence (0-1)
lidar_quality_score	LiDAR quality confidence (0-1)
spatial_quality_score	Spatial quality confidence (0-1)
overall_confidence	Overall confidence score (0-1)
siteID	NEON site code (ABBY, WREF, MLBS, OSBS)
recommendation	Recommended usage
split	Data split assignment

Split files (42 columns each, excluding species and species_raw) are provided for convenience:

• train_set.csv (2,850 samples: train + weak_supervision)
• validation_set.csv (304 samples)
• test_set.csv (35 samples: BC holdout)

Note: The species and species_raw columns are only available in unified_dataset.csv. Use the unified file if you need these columns.

Point cloud patches (.npy files)

Each sample has a corresponding point cloud patch stored as a NumPy .npy array of shape (N, 3) containing [x, y, z] coordinates. Patches are 5m-radius cylinders centered on each tree stem.

Patches are available as a compressed archive:

patches.tar.gz
├── patches/               # BC Vancouver Island (36 files)
├── neon_lidar/patches/    # NEON WREF/ABBY (2,949 files)
└── idtrees/patches/       # IDTReeS competition (1,055 files)

Usage

Load tabular data

import pandas as pd
from datasets import load_dataset

dataset = load_dataset("dfichuk/tree-species-vancouver-island")
df_train = dataset['train'].to_pandas()
df_val = dataset['validation'].to_pandas()
df_test = dataset['test'].to_pandas()

Load point cloud patches

import urllib.request
import tarfile
import numpy as np
import os

# Download and extract patches (~83 MB compressed)
url = "https://huggingface.co/datasets/dfichuk/tree-species-vancouver-island/resolve/main/patches.tar.gz"
tar_path = "patches.tar.gz"

urllib.request.urlretrieve(url, tar_path)
with tarfile.open(tar_path, "r:gz") as tar:
    tar.extractall()

# Load a specific patch
patch_path = df_train.iloc[0]["patch_file"]
patch = np.load(patch_path)
print(f"Patch shape: {patch.shape}")  # (N, 3)

Feature-based classifier

import pandas as pd
from xgboost import XGBClassifier
from sklearn.metrics import classification_report
from datasets import load_dataset

dataset = load_dataset("dfichuk/tree-species-vancouver-island")

train = dataset['train'].to_pandas()
val = dataset['validation'].to_pandas()

feature_cols = [c for c in train.columns if c.startswith(('height_', 'intensity_', 'pct_', 'density_'))]

# Train on PNW-native NEON data
mask = (train['source'] == 'neon') & (train['pnw_native'] == True)
X_train = train.loc[mask, feature_cols]
y_train = train.loc[mask, 'taxonID']
X_val = val[feature_cols]
y_val = val['taxonID']

model = XGBClassifier()
model.fit(X_train, y_train)
print(classification_report(y_val, model.predict(X_val)))

Data Splits

Split	Samples	Description
train	2,850	High-confidence + weak supervision training
validation	304	Held-out validation
test	35	BC Vancouver Island holdout (geographic generalization)
weak_supervision	819	Lower confidence labels (included in train_set.csv)
excluded	32	Samples excluded due to quality concerns

For higher quality training, use recommendation == 'train_high_quality' or filter by overall_confidence >= 0.7.

Quality Scores

Each sample has four component quality scores and an overall confidence score (0-1):

Dimension	Weight	What it measures
Label quality	0.35	Species-level ID vs genus-level vs unknown; expert verification
Measurement quality	0.25	Stem diameter, plant health status, data quality flags
LiDAR quality	0.30	Point density in crown, height consistency, return ratios
Spatial quality	0.10	Whether crown is fully within tile (not on edge)

Overall confidence is the weighted average of available dimensions. Range: 0.3 -- 0.9.

Citation

If you use this dataset, please cite the source datasets:

@misc{neon_data,
  title = {National Ecological Observatory Network Data Products},
  url = {https://www.neonscience.org/data}
}

@software{weinstein2022idtreees,
  author = {Weinstein, Ben G. and others},
  title = {Individual Tree Crowns and Species from NEON (IDTReeS)},
  year = {2022},
  doi = {10.5281/zenodo.5553697}
}

And the derived dataset:

@misc{fichuk2025tree,
  title = {Vancouver Island Tree Species LiDAR Dataset},
  author = {Fichuk, Dexter},
  year = {2025},
  publisher = {Hugging Face}
}

License

CC-BY-4.0. Individual source data may have their own terms (NEON data: CC0, IDTReeS: CC-BY-4.0).

Repository Structure

├── unified_dataset.csv           # All 4,040 samples (44 columns, includes species/species_raw)
├── train_set.csv                 # 2,850 training samples (42 columns)
├── validation_set.csv            # 304 validation samples (42 columns)
├── test_set.csv                  # 35 BC holdout samples (42 columns)
├── unified_metadata.json         # Dataset summary and species list
├── patches.tar.gz                # Point cloud patches (~83 MB)
├── README.md                     # This file
└── download_patches.py           # Patch download and extraction script