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Machine Learning for Climate Science: A hands on Introduction

Aman Bagrecha

Senior Geospatial Scientist

@amanbagrecha

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BNMIT

13-09-2025

Machine Learning for Climate Science | @amanbagrecha | 2025

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Communities

  • Organiser @ Let’s Talk Spatial Community
  • YouTube and Blog @ amanbagrecha.github.io
  • Young Professional Chair IEEE GRSS Bangalore Chapter

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Understanding Satellite Imagery

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How machine learning plays a role

Use cases <> geospatial machine learning

Real World Example on Flood Monitoring

This Session

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What is the Satellite Imagery though?

What relation does it have with Climate?

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Image © CNES/Airbus, Maxar Technologies

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Natural Color (RGB)

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False Color Infrared (NIR, Red, Green)

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True color vs False color

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Normalized Difference Water Index (NDWI)

(Green - NIR) / (Green + NIR)

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How machine learning can be used here?

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  1. Huge amount of satellite + sensor data
  2. Generalization Across Regions & Sensors
  3. Scalable to Global Monitoring

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Object Detection

Natural Calamity

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https://earthobservatory.nasa.gov/images/150555/smoky-skies-in-northern-india

Fire count prediction

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Segmentation/classification

Global Forest Change

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https://google.earthengine.app/view/forest-change

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Air Quality Estimation

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https://abocin.users.earthengine.app/view/air-quality

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Construction of Tehri Dam

1991 - 2010

Time Series Map

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VH

VV/VH

DEM

Segmentation

Flooding

How to estimate the flooding extent

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Natural Language Geocoding

https://element84.com/machine-learning/natural-language-geocoding/

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https://research.google/blog/geospatial-reasoning-unlocking-insights-with-generative-ai-and-multiple-foundation-models/

Vision Language Model

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GraphCast: Google’s Weather Forecasting Model

Earth Nullschool: https://earth.nullschool.net/

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Forecasting

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How does weather forecasting happen?

  • Based on deep neural networks
  • Encoder -> processor -> decoder is popular
  • Trained on reanalysis data (ERA5)
  • Predicts every 6 hours for next 10 days

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https://deepmind.google/discover/blog/graphcast-ai-model-for-faster-and-more-accurate-global-weather-forecasting/

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Tools used for climate analysis

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Real-world Example on Flood Monitoring

What is the extent of flooding?

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Process of Change Detection

Train: Collect data → Preprocess → Train → Validate

Predict: Collect data → Preprocess → Predict (pre and post) → Subtract

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Subset

API

Sentinel-1 RTC +NASADEM

Planetary Computer API

Xarray + Stackstac

4-channel band

VV

VH

VV/VH

DEM

Labelled Data

Collect Data: Generating 4-channel tiles

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Tiling and Augmentation: chips, vertical flips, contrast stretching, sharpening.

Tiling

Augmentation

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4-channel satellite imagery

Training Process

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Inference Process

pre-flood

post-flood

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Thank you

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