Programme Parameters
Advanced Postgrad
3 Days (4.5 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
🎯 Program Aim
“Eyes in the Sky” is a concise, hands-on course on using drones, satellites, and AI for real-time environmental monitoring.
📋 Course Objectives
1. Translate AI in Sustainability & Climate theory into practical, reproducible analysis.
2. Build a defensible project you can showcase to supervisors, reviewers, or employers.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in AI in Sustainability & Climate
• R&D engineers and working professionals applying AI in Sustainability & Climate in industry
• Academics and educators building research or teaching capacity in AI in Sustainability & Climate
🚀 Key Learning Outcomes
• Tangible, reproducible AI in Sustainability & Climate work to show supervisors or employers.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Foundations of Environmental Remote Sensing
Define scope & requirements for environmental monitoring (deforestation, wildfire, air quality). • Examine various platforms and payloads: UAS (RGB/TIR), public satellites (Sentinel-1/2, Landsat, MODIS/VIIRS), and ground AQ sensors (PM₂.₅/NO₂/O₃). • Understand data plumbing techniques including orthorectification, tiling, STAC, and cloud/gap handling.
AI for Deforestation & Wildfire Detection
Implement change detection and early-warning models for deforestation and wildfires. • Apply techniques for wildfire detection: TIR/VIIRS anomaly flags, smoke segmentation, and alert thresholds. • Analyze deforestation using time-series change (BFAST/Delta), semantic segmentation, and accuracy assessment.
Air Quality & Carbon Estimation with AI
Fuse EO (AOD) with ground air quality data, perform bias correction, and nowcast under missing data scenarios. • Estimate carbon and emissions using multispectral+SAR biomass and FRP→emissions relationships, including uncertainty bands. • Explore the Silvanet & Silvaguard case study to understand real-world application of early warning and integration.
Data Fusion and Scaling Challenges
Fuse multi-resolution data (UAV + EO + IoT) for robust signal detection in environmental monitoring. • Address data gaps, accuracy limits, and scaling issues in low-resource contexts effectively. • Implement deployment strategies at scale including robustness, drift monitoring, human-on-the-loop, and low-bandwidth constraints.
Hands-On Lab: End-to-End AOI Build
Create a STAC-indexed Area of Interest (AOI) data lake incorporating Sentinel-2, VIIRS, and drone scene data. • Run a complete pipeline: cloud mask → wildfire/smoke flags → forest-loss polygons (with confidence). • Fuse EO + ground AQ data to produce a daily bias-corrected PM map.
Dashboarding & Decision Support
Estimate stand-level carbon with basic uncertainty for environmental impact assessment. • Publish a lightweight dashboard displaying alerts, loss, AQ index, and carbon snapshots for decision support. • Prototype a minimal alerting workflow and web map for efficient communication of environmental insights.
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Drones |
| Covered Tool / Platform | Sentinel-1/2 |
| Covered Tool / Platform | Landsat |
| Covered Tool / Platform | MODIS/VIIRS |
| Covered Tool / Platform | PM₂.₅/NO₂/O₃ sensors |
| Covered Tool / Platform | STAC |
| Covered Tool / Platform | BFAST/Delta |
| Covered Tool / Platform | Python |
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