README.md

metadata

license: apache-2.0
title: Smokey the Bear Helper
sdk: streamlit
emoji: 🐨
colorFrom: purple
colorTo: purple
short_description: App to detect deforestation and explore satellite embeddings

Streamlit App

A 4-page portfolio app for exploring the forest loss classification project. Runs locally or via Docker.

---

Navigation

A custom sidebar nav (HTML GET-form buttons with active-state highlighting) links the four pages. A region selector below the nav links controls which "other" AOI is shown in supplemental sidebar stats.

---

Pages

Page 1 — Project Overview

Hero section with a Lottie animation, project title, and a one-paragraph summary of the approach.

KPI cards (loaded from resources/cache/kpi_summary.json):

• Pixel-Years, Regions, Features Engineered, Best PR-AUC, Best F1, Brier Score
• PR-AUC Lift metric — improvement over logistic regression baseline

A Tale of Two Forests — side-by-side stat cards comparing Canada (PR-AUC 0.91) and Amazon Basin (PR-AUC 0.62), plus a delta callout. This frames the central question the app explores.

Bonus: Smokey's birthday easter egg fires on August 9th.

---

Page 2 — Explore the Data

AOI radio selector (inline) drives all four tabs. Each tab loads pre-computed parquet/JSON from resources/cache/ via @st.cache_data.

Tab	Contents
📡 Embedding Profiles	Mean embedding delta profile per dimension, grouped by class (Loss / No Loss) or by year. Bar chart of top 10 most divergent embedding dimensions.
🎯 Target Distribution	Pie chart of class distribution. Metric cards: total pixel-years, loss pixel count, no-loss count, class imbalance ratio.
🌊 Drift + Timelapse	Line chart of mean drift magnitude by year. Pixel timelapse — scatter map of sampled pixel locations colored by loss label.
🔬 Drift Explorer	Year selector + violin/histogram of drift magnitude distribution, split by class.

---

Page 3 — Canada vs. Amazon Basin

Focused comparison of the two focus-region XGBoost models (PR-AUC 0.91 vs 0.62) to explore what the performance gap reveals about the data and the signal.

Metrics & Confusion Matrices — side-by-side columns with PR-AUC / F1 / Recall metric cards and Plotly confusion matrices for Canada and Amazon Basin.

Feature Importance (XGBoost Gain) — side-by-side horizontal bar charts of the top 15 features for each model.

Expandable analysis sections:

• Why does Canada score 0.91 and Amazon only 0.62? — narrative covering class balance, the fire year paradox, forest loss vs. deforestation definitions, feature importance as evidence, and cloud cover.
• Best Optuna hyperparameters — Canada vs. Amazon Basin — side-by-side parameter table with trial counts.

Threshold Explorer — AOI radio selector, probability score distribution histogram, interactive threshold slider (0.01–0.99), live confusion matrix, and precision / recall / F1 metric cards. Includes a Precision-Recall curve for the selected region.

---

Page 4 — How I Built This

Two tabs: 🛠️ Build and 💳 Credits.

Build tab:

Pipeline Architecture — animated Plotly pipeline diagram (7 nodes, data-flow animation). A stage selector dropdown below the diagram shows a detail card for each pipeline step (GEE → Feature Engineering → Baseline Experiments → Optuna + MLflow → XGBoost Classifier → Model Evaluation → Streamlit + Docker).

Build Timeline — 8-day numbered SVG timeline cards (Day 1: GEE data acquisition through Day 8: CI/CD pipeline).

Key Decisions & Lessons — six expandable sections covering:

• Same pixel, different years — why the per-(pixel, year) framing beat single snapshots and LSTM sequences
• Why per-region models instead of one global model
• Drift magnitude as the primary engineered feature — why additional features didn't help
• Handling severe class imbalance (balanced weights, PR-AUC, Brier score)
• Geo-stratified train/test split — preventing spatial leakage
• Optuna over grid search — why Bayesian optimization fits this problem

Source Code — GitHub link button.

Credits tab: Data source attributions (AEF, Hansen GFC, RADD), library credits, and acknowledgements.

---

Running the App

Docker (recommended):

docker compose up
# open http://localhost:8501

Local:

pip install -r app/requirements-app.txt
streamlit run app/streamlit_app.py

---

Configuration

• Theme: .streamlit/config.toml — font, primary color, background. Fine-tune colors by running app/theme_preview.py locally.
• Pipeline params: config/config.yaml — AOIs, GEE export settings
• Pre-computed artifacts: resources/cache/ — the app reads JSON metrics and parquet data samples at startup via @st.cache_data; no model inference happens at runtime. Regenerate with python resources/precompute.py.