Eyes in the Sky: AI for Real-Time Environmental Monitoring

Introduce core concepts of AI-enabled remote sensing for real-time monitoring.

Demonstrate ingestion & preprocessing of satellite, drone, and sensor data.

Apply change detection and early-warning models for deforestation and wildfires.

Fuse multi-resolution data (UAV + EO + IoT) for robust signal detection.

Estimate air-quality proxies (e.g., AOD) and vegetation carbon stocks.

Evaluate model performance (IoU/F1/PR) and calibrate alert thresholds.

Explore the Silvanet & Silvaguard case to derive deployable design patterns.

Address data gaps, accuracy limits, and scaling in low-resource contexts.

Prototype a minimal alerting workflow and web map/dashboard for decision support.

Build a real-time EO pipeline (satellite + drones + sensors).

Train models for deforestation change & early wildfire signals.

Fuse multi-resolution data; detect smoke/thermal anomalies.

Estimate air-quality (AOD) and vegetation carbon stocks.

Evaluate (IoU/F1), tune thresholds to cut false alarms.

Deliver: alerting workflow, mini dashboard, and a one-page rollout for low-resource settings.

Environmental & forestry professionals, wildfire/disaster management teams

Remote sensing & GIS analysts (QGIS/ArcGIS/Google Earth Engine users)

Data scientists & ML engineers working with geospatial data

Air-quality, climate, and sustainability researchers/practitioners

UAV/drone operators and Earth-observation startups

Government agencies, utilities, and NGOs involved in monitoring & response

Graduate students and faculty in Environmental Science, Geography, CS/AI