Programme Parameters
Graduate / Intermediate
3 Days (4.5 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Deep Learning for Earth Observation: From Multi-Terabyte Networks tackles the challenge of applying deep learning to enormous Earth-observation datasets. You learn to work with large satellite and climate data (including formats like NetCDF), build data pipelines that handle terabyte-scale imagery, and train deep models for land, atmosphere and environmental-change tasks. The course emphasises the scale problem — efficient loading, tiling and distributed training — alongside the modelling. You finish able to reason about a large-scale deep-learning Earth-observation pipeline. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers deep learning for Earth observation at scale — working with multi-terabyte satellite and climate datasets to model land, atmosphere and environmental change.
📋 Course Objectives
1. Work with large satellite and climate datasets.
2. Handle formats like NetCDF at scale.
3. Build terabyte-scale data pipelines.
4. Train deep models for Earth observation.
5. Apply efficient and distributed training.
👥 Who Should Enroll?
• Earth-observation and climate data scientists
• Remote-sensing and geospatial engineers
• ML engineers with large imagery
• Students of geospatial deep learning
🚀 Key Learning Outcomes
• An understanding of large-scale EO deep learning.
• A big-data geospatial perspective.
• A scalable Earth-observation project.
• 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 – Data Engineering & Preparation
Ingest multi‑terabyte NetCDF/HDF5 files using Xarray • Chunk, lazy‑load, and create memory‑safe pipelines • Regrid and interpolate multi‑source climate fields
Day 2 – Core AI: ConvLSTM Modeling
Frame anomaly forecasting as a spatiotemporal task • Build and train a ConvLSTM network on climate tensors • Implement windowing, batching, validation, and checkpointing
Day 3 – Visualization, Evaluation & Publication
Generate interactive heatmaps of forecasted anomalies • Create publication‑ready maps with projections and overlays • Compute RMSE and spatial correlation metrics for research reporting
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Xarray |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Cartopy |
| Covered Tool / Platform | MLflow |
| Covered Tool / Platform | NetCDF/HDF5 |
Frequently Asked Questions
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