Tutorials

Disaster Risk Monitoring Using Satellite Imagery

Learn how to build and deploy a deep learning model to automate the detection of flood events using satellite imagery. This workflow can be applied to lower the cost, improve the efficiency, and significantly enhance the effectiveness of various natural disaster management use cases.

Societal Impacts Intermediate Python Computer Vision Disaster Management Satellite Imagery

ClimateLearn: Machine Learning for Predicting Weather and Climate

Apply machine learning to predict climate variables into the future and transform low-resolution outputs of climate models into high-resolution regional forecasts.

Climate Prediction Introductory Python Climate Modeling Time Series Analysis Weather

FourCastNet: A practical introduction to a state-of-the-art deep learning global weather emulator

Learn how to use FourCastNet, a weather model based on deep learning, to obtain short to medium-range forecasts of crucial atmospheric variables such as surface wind velocities.

Climate Prediction Intermediate Python Climate Modeling Time Series Analysis Weather

Automating the Creation of LULC Datasets for Semantic Segmentation

Learn how to create a custom LULC dataset by combining multispectral satellite and vector data.

Farms & Forests Intermediate Python Computer Vision Remote Sensing Land Use Land Classification (LULC)

Open Catalyst Project: An Introduction to Machine Learning for Material Discovery

Chemical reactions are one approach to convert intermittently available renewable energy, such as solar or wind, to other fuels, like hydrogen. Learn how ML can discover low-cost catalysts to drive these reactions at high rates.'

Check out the recorded talk for more information.

Electricity Systems Introductory Python Material Science Graph Neural Networks

Research Synthesis using NLP in the Field of Climate Change

Explore how Natural Language Processing (NLP) can be used to identify and map climate-relevant literature.

Education Introductory Python Natural Language Processing Academic Literature Data Science

Forecasting the El Nino/ Southern Oscillation with Machine Learning

Existing El Niño forecasts use dynamical models that rely on the physics of the atmosphere and ocean. Learn how to create El Niño forecasts using machine learning instead, which uses statistical optimization to issue forecasts.

Check out the introductory slides for more information.

Climate Prediction Introductory Python Time Series Analysis Weather Climate Modeling

Building Load Forecasting with Machine Learning

Accurate forecasts of energy demand and supply are essential to mitigate climate change. Discover how to train and evaluate building load forecasts using off-the-shelf ML models.

Buildings & Cities Intermediate Python Time Series Analysis Energy Management

Building Control with RL using BOPTEST

Apply reinforcement learning to a building emulator to intelligently control HVAC systems.

Check out the recorded talk for more information.

Buildings & Cities Intermediate Python Reinforcement Learning Energy Management

Land Use and Land Cover (LULC) Classification using Deep Learning: Part I

Mapping the extent of land use and land cover categories over time is essential for better environmental monitoring, urban planning and nature protection. Train and fine-tune a deep learning model to classify satellite images into 10 LULC categories.

Check out the introductory slides for more information.

Farms & Forests Intermediate Python Deep Learning Computer Vision Remote Sensing Land Use Land Classification (LULC)

Land Use and Land Cover (LULC) Classification using Deep Learning: Part II

Use the neural network trained in LULC Classification Part I to generate land use and land cover maps with Python GIS.

Check out the introductory slides for more information.

Farms & Forests Intermediate Python Geospatial Deep Learning Computer Vision Remote Sensing Land Use Land Classification (LULC)

Practical ML Tools for CC

This series of tutorials will teach you to apply regression and classification techniques to problems in the climate space.
Module 0.0: Learn how to pattern match within images.
Module 0.1: Train a CNN to determine when a solar cell is defective.
Module 1.0: Learn the basics of working with Python in a Google Colab notebook.
Module 1.1: Explore the relationship between CO2 and worldwide temperatures.
Module 1.2: Estimate a function for the time variation of global temperature using periodic expressions and first principles.
Module 1.3: Determine the likelihood that current CO2 levels are consistent with recent history on Earth.
Module 1.4: Learn how to apply Bayesian time series modeling to predict rising global temperatures.

Check out the introductory slides for more information.

Climate Prediction Introductory Python Solar Energy Deep Learning Climate Modeling Time Series Analysis