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.
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.
What Participants Will Learn
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
Structure
📅 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
