| --- |
| license: mit |
| library_name: sklearn |
| tags: |
| - remote-sensing |
| - tree-canopy |
| - sentinel-2 |
| - philippines |
| - metro-manila |
| - civic-technology |
| pipeline_tag: tabular-classification |
| --- |
| |
| # Leaves.PH canopy classifier |
|
|
| The published per-pixel canopy model behind [leaves.ph](https://leaves.ph), an open, reproducible tree-cover map of Metro Manila (17 NCR cities plus Pateros), 2019 to 2026. |
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| 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). |
|
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| ## Features (10) |
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| `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. |
|
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| ## Evaluation |
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| 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 | |
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| 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. |
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| ## Intended use and limits |
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| - 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 |
|
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| - `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 |
|
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| ## Links and citation |
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| - 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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|
