Tutorials
Sea Water Flood Risk Assessment in Egypt using Deep Learning, Sentinel-1 & 2, and Copernicus DEM: Part I
Floods in coastal areas can be extremely destructive natural hazards resulting in societal and economical damage. In this tutorial, explore how to predict building and population density to understand the potential impact from a flooding event.
Sea Water Flood Risk Assessment in Egypt using Deep Learning, Sentinel-1 & 2, and Copernicus DEM: Part II
Part II of this tutorial series demonstrates how train a CNN on satellite data to detect the extent of flooding in a potential disaster.
Reducing your Climate Impact when Training ML Models
Learn how to measure a machine learning model's carbon footprint and practice strategies that can help shrink the energy involved in training these models.
Introduction to Camera Trap Recognition with Deep Learning
The greatest logistical barrier to long-term wildlife monitoring with camera traps is the overwhelming amount of human labor needed to annotate thousands or millions of images for ecological analysis. In this tutorial, learn how to train a deep learning model to automatically identify animals in camera trap images.
Predicting Mobility Demand from Urban Features
Ensuring a shared bike is readily available can reduce demand for less climate-friendly transportation options. Explore how to model the relationship between bike usage and points of interest (POIs) to identify the best locations for new shared-bike stands.
AI for Optimal Power Flow
AC Optimal Power Flow (OPF) attempts to determine the setpoints of generators that would minimize the operating cost of a power system while meeting other operational constraints. In this tutorial, learn how to leverage PyTorch to train a neural network to approximate the optimal solutions.
NLP Models for Climate Policy Analysis: Part I
Explore how Natural Language Processing (NLP) can be used to assist in identifying and mapping climate-relevant literature using a supervised learning approach.
NLP Models for Climate Policy Analysis: Part II
Build on what you learned in Part I, and leverage a state of the art Large Language Model (LLM) to classify climate policy documents.
Estimating Coal Power Plant Operation From Satellite Images with Computer Vision
Explore how to monitor coal power plant activity by leveraging satellite imagery and computer vision models.
Smart Meter Data Analytics: Practical Use-Cases and Best Practices of Machine Learning Applications for Energy Data in the Residential Sector
A practical guide to current trends in smart meter data analytics with a focus on feature engineering and machine learning scenarios for energy data at 15-minute resolution. Gain insights into current trends and use cases in the energy field and get a sense of typical and atypical energy consumption in a residential building.
Quantus x Climate: Applying Explainable AI Evaluation in Climate Science
In climate science, explainable artificial intelligence (XAI) can be used to improve and validate deep learning methods, but evaluation and selection of XAI methods is challenging. Learn how to use the explainable AI evaluation package Quantus to compare and select an appropriate XAI for your climate AI research task.
CityLearn: Reinforcement Learning Control for Grid-Interactive Efficient Buildings and Communities
Learn how to design simple and advanced control algorithms to provide energy flexibility, and acquire familiarity with the CityLearn environment and its datasets for extended use in projects. The tutorial provides a walk-through on how to set up and interact with the environment using a real-world dataset in three hands-on control experiments.
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.
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.
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.
Automating the Creation of LULC Datasets for Semantic Segmentation
Learn how to create a custom LULC dataset by combining multispectral satellite and vector data.
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.
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.
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.
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.
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.
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.
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.