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

Course Description

Eyes in the Sky is a practical, application-focused course on using drones, satellites, and AI to support real-time environmental monitoring. Using the Silvanet & Silvaguard (Germany) case study, participants learn how remote sensing and machine learning can be applied to detect deforestation and wildfire risks, assess air-quality indicators, and estimate emissions and vegetation carbon stocks. The course also addresses real deployment constraints such as cloud cover, missing data, model accuracy, limited connectivity, and scaling challenges in low-resource regions. Participants will build a minimal monitoring workflow and publish outputs through a lightweight dashboard for decision support.

Aim

To equip participants with the skills to design, evaluate, and deploy AI-driven environmental monitoring pipelines that combine drone imagery, satellite observations, and sensor data for timely detection of deforestation, wildfires, and air-quality issues, based on the Silvanet & Silvaguard case study, and aligned with practical approaches for handling data gaps, accuracy limits, and scalable deployment.

Course Objectives

Participants will be able to:

• Understand the fundamentals of AI-enabled remote sensing for operational monitoring.

• Ingest and preprocess satellite imagery, drone data, and ground sensor datasets.

• Apply change detection and early-warning methods for deforestation and wildfire monitoring.

• Combine UAV, Earth observation, and IoT/sensor data for more reliable detection signals.

• Generate air-quality proxy maps (e.g., using AOD-based indicators) and estimate vegetation carbon stocks.

• Evaluate model performance using IoU, F1 score, and precision–recall metrics, and set alert thresholds appropriately.

• Extract deployable system patterns from the Silvanet & Silvaguard implementation and adapt them to other contexts.

• Address challenges related to missing data, uncertainty, limited bandwidth, and scaling in low-resource environments.

• Prototype a basic alerting workflow and publish results through a web map or dashboard for decision-making.

Course Structure

Module 1: Platforms, Sensors, and Mission Design

• Monitoring scope and requirements for deforestation, wildfire risk, and air quality (latency, resolution, revisit frequency)

• Platforms and payloads: UAV (RGB/thermal), satellite systems (Sentinel-1/2, Landsat, MODIS/VIIRS), ground air-quality sensors (PM₂.₅/NO₂/O₃)

• Data preparation workflow: orthorectification, tiling, STAC cataloging, spatiotemporal indexing, cloud handling, and missing-data strategies

• Case study overview: Silvanet & Silvaguard (Germany)—system design, integration approach, limitations

• Governance and ethics: privacy considerations, community data rights, and responsible alerting practices

Module 2: Analytics and Multi-Source Fusion

• Wildfire monitoring: thermal anomaly detection (TIR/VIIRS), smoke segmentation, change detection, alert threshold calibration

• Deforestation monitoring: time-series change detection (BFAST/Delta), segmentation workflows, validation and accuracy assessment

• Air quality monitoring: satellite AOD and ground sensor fusion, bias correction, nowcasting under incomplete data

• Carbon and emissions estimation: multispectral and SAR-based biomass estimation, FRP-based emissions estimation, uncertainty reporting

• Scaling and reliability: robustness, drift monitoring, human-in-the-loop review, low-bandwidth deployment strategies

Module 3: Hands-On Implementation (End-to-End AOI Build)

• Build a STAC-indexed AOI data lake using Sentinel-2, VIIRS, and a sample drone scene

• Execute a complete pipeline: cloud masking → wildfire/smoke flags → forest-loss polygons with confidence scoring

• Fuse Earth observation and ground data to generate a daily PM map with bias correction

• Estimate stand-level carbon with uncertainty handling

• Publish outputs through a lightweight dashboard: alerts, forest-loss layer, air-quality index, and carbon snapshot

Who Should Enrol

• Environmental and forestry professionals, and wildfire/disaster management teams

• Remote sensing and GIS analysts (QGIS, ArcGIS, Google Earth Engine users)

• Data scientists and ML engineers working with geospatial and Earth observation datasets

• Researchers and practitioners in air quality, climate, and sustainability

• UAV/drone operators and Earth observation startups

• Government agencies, utilities, and NGOs involved in monitoring, compliance, and response

• Graduate students and faculty in Environmental Science, Geography, Computer Science, and AI