Deep Learning for Earth Observation: From Multi-Terabyte NetCDF to Anomaly Forecasting
Discover how deep learning is revolutionizing Earth observation, enabling the analysis of massive NetCDF files and predicting anomalies in environmental data.
Skills you will gain:
About Program:
Climate anomalies such as heatwaves, floods, droughts, and extreme precipitation events are increasing in frequency and intensity due to climate change. Accurate prediction of these anomalies requires models that can capture complex spatial patterns and temporal dynamics across large-scale environmental datasets.
This workshop focuses on the application of spatiotemporal deep learning techniques to climate anomaly prediction. Participants will explore how advanced AI models—including CNNs, RNNs, LSTMs, ConvLSTMs, Transformers, and Graph Neural Networks—can be used to analyze satellite imagery, geospatial datasets, and time-series climate data.
Aim: The aim of this workshop is to equip participants with the theoretical foundations and practical skills required to design, implement, and evaluate spatiotemporal deep learning models for accurate climate anomaly prediction.
Program Objectives:
- Understand key climate & geospatial datasets for anomaly prediction
- Apply deep learning for spatial pattern learning (CNNs)
- Model temporal dynamics (LSTM/Transformers)
- Build spatiotemporal models (e.g., ConvLSTM) for forecasting anomalies
- Evaluate and interpret predictions using standard metrics and tools
What you will learn?
📅 Day 1 — The Setup & Data Prep
- Focus: Large-Scale Climate Data Engineering
- Key Topics:
- Ingesting and slicing multi-terabyte NetCDF/HDF5 datasets efficiently
- Chunking, lazy loading, and memory-safe workflows for scalable pipelines
- Spatial interpolation / regridding to align multi-source climate fields
- Handling temporal missing values and gaps using practical imputation strategies
- Hands-on (Jupyter Notebook):
- Notebook Title: Climate Data Pipeline Builder
- Task: Load raw climate datasets using Xarray, preprocess spatial–temporal grids, and generate a clean, model-ready tensor dataset
- Deliverable: A clean, model-ready spatiotemporal dataset pipeline
📅 Day 2 — The Core AI / Tech Implementation (ConvLSTM)
- Focus: Spatiotemporal Deep Learning for Anomaly Forecasting
- Key Topics:
- Framing anomaly prediction as a spatiotemporal forecasting problem
- Building and training a ConvLSTM network
- Learning spatial features (maps/grids) and temporal dynamics (time-series evolution)
- Training workflow: windowing, batching, validation strategies, and checkpoints
- Hands-on (Jupyter Notebook):
- Notebook Title: ConvLSTM Climate Forecast Engine
- Task: Train a ConvLSTM model on prepared climate tensors and generate anomaly forecasts
- Deliverable: A trained ConvLSTM model producing climate anomaly predictions
📅 Day 3 — Tangible Output & Paper Readiness
- Focus: Visualization, Evaluation & Publication-Ready Outputs
- Key Topics:
- Generating automated, interactive heatmaps of forecasted anomalies
- Creating publication-ready visual outputs with map projections and overlays
- Extracting standard evaluation metrics for methodology sections:
- RMSE (Root Mean Square Error)
- Spatial Correlation
- Hands-on (Jupyter Notebook):
- Notebook Title: Climate Results & Metrics Generator
- Task: Generate evaluation metrics, anomaly maps, and export reproducible figures for research reporting
- Deliverable: A paper-ready results bundle (figures + metrics + reproducible workflow)
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
- Doctoral Scholars & Researchers: PhD candidates seeking to integrate computational workflows into their molecular research.
- Postdoctoral Fellows: Early-career scientists aiming to enhance their data-driven publication profile.
- University Faculty: Professors and HODs interested in modern bioinformatics pedagogy and tool mastery.
- Industry Scientists: R&D professionals from the Biotechnology and Pharmaceutical sectors transitioning to genomic-driven discovery.
- Postgraduate Students: Final-year PG students looking for specialized research-grade exposure beyond standard curricula.
Career Supporting Skills
Program Outcomes
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Analyze and preprocess climate and geospatial time-series datasets
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Develop spatiotemporal deep learning models for anomaly detection
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Implement CNN, LSTM, Transformer, and hybrid architectures for climate forecasting
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Build a prototype AI-based climate anomaly prediction system
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Evaluate model accuracy, uncertainty, and interpretability for real-world applications
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Apply AI-driven insights to support climate risk assessment and resilience planning