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

To equip participants to design, evaluate, and deploy AI-driven, real-time environmental monitoring pipelines that fuse drone and satellite imagery for rapid detection of deforestation, wildfires, and air-quality issues—grounded in the Silvanet & Silvaguard (Germany) case study—and to develop practical strategies for overcoming data gaps, accuracy limits, and scalability challenges in low-resource settings.

• 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.

📅 Day 1 – Platforms, Sensors & Mission Design

• Scope & requirements: deforestation, wildfire, air quality (latency, resolution, revisit)
• Platforms/payloads: UAS (RGB/TIR), public satellites (Sentinel-1/2, Landsat, MODIS/VIIRS), ground AQ (PM₂.₅/NO₂/O₃)
• Data plumbing: orthorectification, tiling, STAC, spatiotemporal indexing, cloud/gap handling
• Case study overview: Silvanet & Silvaguard (Germany) — early warning, integration, limitations
• Governance/ethics: privacy, community data rights, responsible alerting

📅 Day 2 – Analytics & Fusion

• Wildfire: TIR/VIIRS anomaly flags, smoke segmentation, change detection, alert thresholds
• Deforestation: time-series change (BFAST/Delta), semantic segmentation, accuracy assessment
• Air quality: EO AOD ↔ ground fusion, bias correction, nowcasting under missing data
• Carbon/emissions: multispectral+SAR biomass, FRP→emissions, uncertainty bands
• Deployment at scale: robustness, drift monitoring, human-on-the-loop, low-bandwidth constraints

📅 Day 3 – Hands-On: End-to-End AOI Build (single lab)

• Create STAC-indexed AOI data lake (Sentinel-2 + VIIRS + one drone scene)
• Run pipeline: cloud mask → wildfire/smoke flags → forest-loss polygons (with confidence)
• Fuse EO + ground AQ to produce daily PM map (bias-corrected)
• Estimate stand-level carbon with basic uncertainty
• Publish a lightweight dashboard (alerts, loss, AQ index, carbon snapshot)

• 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

• 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.