Leaves.PH canopy classifier

The published per-pixel canopy model behind leaves.ph, an open, reproducible tree-cover map of Metro Manila (17 NCR cities plus Pateros), 2019 to 2026.

A HistGradientBoostingClassifier (scikit-learn) that labels each 30 m Sentinel-2 pixel as canopy or not, trained on 656 hand-labeled high-resolution pixels. It is the source of every figure on the site: the map, the per-LGU and per-barangay series, and the headline NCR canopy percentage (about 9 to 10 percent).

Features (10)

ndvi, dw (Dynamic World tree probability), meta_h (Meta v2 1 m canopy height), esatree (ESA WorldCover tree class), the raw Sentinel-2 bands red / nir / green / blue, plus gndvi and nir_red. Decision threshold 0.5, calibrated so 2021 matches the 10.1 percent human-truth canopy.

Evaluation

Scored against the 656 manual labels under region-grouped out-of-fold cross-validation with post-stratified population weighting:

Model	Precision	Recall	F1	IoU
This classifier (10 features)	0.77	0.79	0.78	0.64
Four-feature model (no spectral bands)	-	-	0.75	-
NDVI > 0.62 baseline	0.69	0.67	0.68	0.52

The raw green and blue bands are what lift it over the baseline: they let it reject high-NDVI grass and scrub the NDVI threshold over-called (precision 0.67 to 0.77), and it removes the year-to-year sawtooth the fixed threshold produced. A CLIP ViT-L/14 embedding was tested as a feature and did not help, so it was dropped.

Intended use and limits

It is a tree-canopy estimate with a known grass/scrub margin, not a per-tree census and not a parcel-level land-use tool.
"Canopy" is a dense-vegetation proxy. Roughly half the flagged area is verified tree canopy above 5 m.
The threshold is calibrated to one epoch (Meta's source imagery is mostly 2018 to 2020), so read the per-year values as annual cross-sectional snapshots, not a validated change series.

Files

canopy_clf.joblib - the trained classifier
canopy_clf_meta.json - features, threshold, training size
master_labels.csv - the 656 gold labels
model_comparison.json - the ablation it was selected from
RESULTS.md - the build writeup

Links and citation

Code and pipeline: https://github.com/xmpuspus/leaves-ph (MIT)
Data products on HuggingFace: https://huggingface.co/datasets/xmpuspus/leaves-ph (CC-BY-4.0)
Methodology: https://leaves.ph/methodology
Cite: Leaves.PH (2026, v0.7.0). https://doi.org/10.5281/zenodo.20470306

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