Program Structure

Module 1: Sustainability Problems + AI Fit

• Where AI helps: prediction, monitoring, optimization, decision support.
• Common data types: tabular, time-series, geospatial, images.
• Project framing: baseline, KPI, constraints, impact.
• Common pitfalls: weak ground truth, overfitting, biased data.

Module 2: Data Foundations (Sensors, GIS, Remote Sensing)

• IoT sensor data: sampling, noise, missing values.
• GIS basics: coordinates, projections (intro), shapefiles/GeoJSON concepts.
• Remote sensing basics: bands, indices (NDVI concept), resolutions (overview).
• Data pipelines: collection → cleaning → storage → analysis.

Module 3: Supervised Learning for Environmental Prediction

• Regression/classification for environment metrics.
• Feature engineering: weather, seasonality, land-use, lag features.
• Model options: trees, gradient boosting, basic neural nets (overview).
• Evaluation: MAE/RMSE, F1/AUC, calibration concepts.

Module 4: Time-Series Forecasting

• Forecasting air quality, demand, rainfall proxies (dataset-based).
• Train/test splits for time-series and leakage prevention.
• Baseline models vs ML forecasting; error analysis.
• Uncertainty and confidence bands (intro).

Module 5: Anomaly Detection & Early Warning

• Detecting spikes: pollution events, leaks, sensor faults.
• Methods: thresholds, rolling stats, isolation forest concepts (intro).
• Alert design: sensitivity vs false alarms.
• Validation using events logs and domain checks.

Module 6: Geospatial AI (Hotspots and Risk Mapping)

• Spatial joins and grid-based analysis (intro).
• Hotspot mapping and clustering concepts.
• Risk layers: flood/heat vulnerability concepts (intro).
• Ethics: using maps responsibly and avoiding misuse.

Module 7: Computer Vision for Environment (Intro)

• Land cover classification concepts and workflows.
• Change detection: deforestation/urban growth concepts.
• Basic segmentation/detection ideas (intro).
• Labeling and ground truth challenges.

Module 8: Deployment, Monitoring & Impact Reporting

• From notebook to app/API: simple deployment path.
• Monitoring: drift, sensor changes, seasonality shifts.
• Impact metrics: emissions avoided, energy saved, risk reduced (conceptual).
• Reporting: transparency, limitations, reproducibility.

Final Project

• Pick one problem: air quality, water quality, energy, waste, land-use, biodiversity.
• Deliverables: dataset + model + evaluation + dashboard/report + impact KPIs.
• Optional: simple app for predictions and alerts.