Eyes in the Sky: AI for Real-Time Environmental Monitoring
From Pixels to Protection: Real-Time AI for Earth’s Alarms
Skills you will gain:
About Program:
Eyes in the Sky” is a concise, hands-on workshop on using drones, satellites, and AI for real-time environmental monitoring. Through the Silvanet & Silvaguard case study, you’ll see how remote sensing and ML fuse to detect deforestation and wildfires, track air quality, and estimate emissions and vegetation carbon stocks—while addressing real-world hurdles like data gaps, accuracy, and scaling in low-resource regions.
Aim: 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.
Program Objectives:
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Introduce core concepts of AI-enabled remote sensing for real-time monitoring.
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Demonstrate ingestion & preprocessing of satellite, drone, and sensor data.
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Apply change detection and early-warning models for deforestation and wildfires.
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Fuse multi-resolution data (UAV + EO + IoT) for robust signal detection.
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Estimate air-quality proxies (e.g., AOD) and vegetation carbon stocks.
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Evaluate model performance (IoU/F1/PR) and calibrate alert thresholds.
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Explore the Silvanet & Silvaguard case to derive deployable design patterns.
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Address data gaps, accuracy limits, and scaling in low-resource contexts.
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Prototype a minimal alerting workflow and web map/dashboard for decision support.
What you will learn?
📅 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)
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
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Environmental & forestry professionals, wildfire/disaster management teams
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Remote sensing & GIS analysts (QGIS/ArcGIS/Google Earth Engine users)
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Data scientists & ML engineers working with geospatial data
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Air-quality, climate, and sustainability researchers/practitioners
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UAV/drone operators and Earth-observation startups
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Government agencies, utilities, and NGOs involved in monitoring & response
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Graduate students and faculty in Environmental Science, Geography, CS/AI
Career Supporting Skills
Program Outcomes
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Build a real-time EO pipeline (satellite + drones + sensors).
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Train models for deforestation change & early wildfire signals.
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Fuse multi-resolution data; detect smoke/thermal anomalies.
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Estimate air-quality (AOD) and vegetation carbon stocks.
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Evaluate (IoU/F1), tune thresholds to cut false alarms.
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Deliver: alerting workflow, mini dashboard, and a one-page rollout for low-resource settings.