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.
About This Course
This 3‑day hands‑on course explores the integration of AI with satellite data for weather forecasting, climate monitoring, and practical applications in agriculture and urban planning. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This 3‑day hands‑on course explores the integration of AI with satellite data for weather forecasting, climate monitoring, and practical applications in agriculture and urban planning.
📋 Course Objectives
1. Put AI Enablement techniques to work on real datasets and case studies.
2. Assemble a documented case study that evidences your applied capability.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in AI Enablement
• R&D engineers and working professionals applying AI Enablement in industry
• Academics and educators building research or teaching capacity in AI Enablement
🚀 Key Learning Outcomes
• A portfolio-grade AI Enablement deliverable you can defend and extend.
• 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
Day 1 – Introduction to Satellite Data & AI for Image Processing
Explore satellite data types, multispectral & hyperspectral imagery • Apply AI preprocessing techniques (filtering, normalization, feature extraction) • Prepare satellite imagery for machine‑learning pipelines • Hands‑on: Preprocess and analyze imagery using Python (OpenCV, scikit‑image)
Day 2 – Predictive Models for Weather Forecasting & Climate Monitoring
Design regression, neural‑network, and LSTM models for weather prediction • Utilize satellite data to monitor long‑term climate trends • Evaluate model accuracy and robustness for real‑time forecasts • Hands‑on: Build and test a weather‑forecasting model with real datasets
Day 3 – AI Tools for Agriculture & Urban Planning
Implement AI for crop‑health monitoring and precision agriculture • Apply AI to analyze urban growth, land‑cover, and environmental management • Create decision‑support systems that turn satellite data into policy insights • Hands‑on: Develop a decision‑support tool for agriculture and urban planning
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | OpenCV |
| Covered Tool / Platform | scikit-image |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Pandas |
| Covered Tool / Platform | NumPy |
| Covered Tool / Platform | GIS |
| Covered Tool / Platform | Google Earth Engine |
Frequently Asked Questions
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