Tutorials
Introduction to AI: Predicting Tsunami Alerts from Earthquake Observations
Build and evaluate an introductory machine-learning model using USGS earthquake observations to identify earthquakes likely to generate tsunami alerts.
Simulating Climate Futures in En-ROADS
Use the En-ROADS climate simulation model to construct emissions-reduction policy scenarios targeting warming below 2°C (ideally 1.5°C), and evaluate implementation trade-offs.
AI for Climate Finance: Estimating Hurricane Wind Value-at-Risk from an AI Weather Ensemble
Estimate hurricane wind value-at-risk using an ensemble of AI weather forecasts and financial exposure information.
Statistical Downscaling of Climate Projections with Deep Learning
Train and evaluate a convolutional neural network on New Zealand CORDEX ML-Bench precipitation data to downscale coarse GCM projections into higher-resolution local climate information.
Detecting Deforestation from Satellite Image Time Series
Detect tropical deforestation from Sentinel-2 NDVI time series with Brazilian Amazon PRODES labels, extending to AlphaEarth embeddings and a time-series Transformer.
Monitoring Coastal Water Quality and Marine Ecosystem Change Using Satellite Remote Sensing and Machine Learning
Use satellite observations and machine learning to assess coastal water quality and marine ecosystem changes.
Hands-On Outbreak Analytics: Dengue Forecasting with RAG and LLM Evaluation
Create a dengue outbreak analytics workflow that integrates prediction, retrieval-augmented generation and evaluation of LLM-generated responses.
A Bird Bioacoustic Monitoring Pipeline — The Lkotkote Case Study
Build a bird-bioacoustics monitoring pipeline for the Lkotkote case study, integrating conservation-oriented AI with locally grounded knowledge.
Question Answering over Sustainability Reports: Information Richness and Answer Quality
In this tutorial, we learn about an advanced strategy for information retrieval for question answering with Large Language Models (LLMs) in knowledge-intensive domains like sustainability reporting. We analyze the quality and quantity of sources before answering a question and quantify uncertainty around answering a question after LLM generation.
Climate Policy Radar's Open Knowledge Graph
Climate Policy Radar (CPR) helps people access and understand vast amounts of climate documents: laws, policies, NDCs, corporate transition plans, litigation documents, reports by statutory advisory bodies and industry bodies, and more. By linking this expert knowledge of climate change to the extensive curated database of climate documents, we show you how to create a climate policy knowledge graph. This can then be used in turn to analyse the global policy landscape.
PiggyCast - Improving Weather Prediction Accuracy through a Stacking-Based Ensemble AI Approach
Stack IFS HRES, GraphCast, Pangu-Weather and NeuralGCM weather forecasts with XGBoost; use SHAP to explain the ensemble’s predictions.
Flood mapping with optical and microwave satellite data: From indices to machine learning
Flood mapping is a critical component of disaster response and climate adaptation, but it is often hindered by cloud cover, ambiguous spectral signals, and trade-offs in image resolution. This tutorial introduces participants to practical methods for flood detection using freely available multi-sensor satellite data. We begin with optical imagery from MODIS and Sentinel-2, showing how spatial resolution affects the level of detail captured in flood maps. We then move to microwave data from Sentinel-1 SAR, which can penetrate clouds and provide more reliable monitoring during flood events. Participants will implement rule-based approaches such as spectral indices, band ratios, and Z-score anomalies, followed by machine learning models including Logistic Regression and Random Forests. Finally, we demonstrate how trained models can be applied to flood events in different parts of the world, highlighting both the opportunities and limitations of generalizing across geographies. By the end of the tutorial, participants will understand how different satellite data sources and modeling approaches influence flood mapping outcomes, and how these methods can support disaster response and long-term climate resilience.
Agricultural Monitoring with Fields of The World (FTW)
Use Fields of The World data and pretrained geospatial models to delineate agricultural field boundaries, classify crop types, and monitor forest loss.
AI‑Powered Measurement & Verification: Building Interpretable Counterfactual Models to Verify Energy Savings in Buildings
Buildings use a lot of energy, but proving that retrofits actually save it is hard. This tutorial is a hands-on guide to make energy efficiency measurable, investable, and a reliable climate asset, using Machine Learning. The goal is to construct interpretable counterfactual baselines from smart-meter and weather data, quantify savings with defensible uncertainty, and communicate results that meet IPMVP and ASHRAE Guideline 14.
One Prompt Fits All: Visual Prompt-Tuning for Remote Sensing Segmentation
This tutorial This tutorial introduces the Prompt-Tuned Segment Anything Model (PT-SAM) for large-scale image segmentation in climate research, leveraging minimal manual input for effective satellite imagery analysis. Participants will learn how to train PT-SAM with lightweight prompt embeddings, enabling accurate, scalable segmentation tasks, such as forest cover analysis and urbanization monitoring, with limited hardware requirements and small datasets.
Understanding Drivers of Climate Extremes Using Regime-specific Causal Graphs
The goal of this tutorial is to show how we can use methods from constraint-based causal discovery to uncover the causal relationships that are present in different moisture regimes. In doing that, we aim to improve our general understanding of the dynamics of extreme events, with application to understanding drivers of soil-moisture under different, more extreme, regimes.
Empowering Safe Reinforcement Learning for Power System Control with CommonPower
This tutorial introduces the CommonPower library, designed to benchmark safe reinforcement learning (RL) algorithms on control problems for power systems. We highlight two crucial issues in RL for power system control: safeguarding RL decision-making and assessing the impact of forecast quality on control performance. Participants will learn how to use CommonPower to further invesitigate these topics.
Planning for Floods & Droughts: Intro to AI-Driven Hydrological Modeling
Use the CAMELS dataset of 531 continental US watersheds to build streamflow prediction models for flood and drought planning.
Agile Modeling for Bioacoustic Monitoring
Develop efficient wildlife bioacoustic classifiers using transfer learning, audio search, and human-in-the-loop active learning.
Aquaculture Mapping: Detecting and Classifying Aquaculture Ponds using Deep Learning
Mananging aquaculture ponds is vital for environmental monitoring and conservation. This tutorial presents how to leverage satellite imagery and semantic segmentation models to detect and map aquaculture ponds based on production intensity.
Zero-Emission Vehicle Intelligence (ZEVi): Effectively Charging Electric Vehicles at Scale Without Breaking Power Systems (or the Bank)
This tutorial surveys different methods to formulate electric vehicle (EV) charging and energy dispatch as an optimization problem, using tools such as convex optimization, Markov decision process (MDP), and reinforcement learning (RL).
Sea Water Flood Risk Assessment in Egypt using Deep Learning, Sentinel-1 & 2, and Copernicus DEM: Part I
Assess coastal flooding exposure in Egypt using Sentinel-1/2 and Copernicus elevation data, including mapping population or building density and CNN-based flood detection.
Sea Water Flood Risk Assessment in Egypt using Deep Learning, Sentinel-1 & 2, and Copernicus DEM: Part II
Assess coastal flooding exposure in Egypt using Sentinel-1/2 and Copernicus elevation data, including mapping population or building density and CNN-based flood detection.
Reducing your Climate Impact when Training ML Models
Measure the carbon footprint of training machine learning models and practise strategies to reduce energy use and associated emissions.
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
Model associations between bike-share usage and nearby points of interest to propose new shared-bike station locations.
AI for Optimal Power Flow
Train a PyTorch neural network to approximate solutions to alternating-current optimal power flow subject to power-system operational constraints.
NLP Models for Climate Policy Analysis: Part I
Use supervised NLP models to identify climate-related literature (Part I), then large language models to classify climate-policy documents (Part II).
NLP Models for Climate Policy Analysis: Part II
Use supervised NLP models to identify climate-related literature (Part I), then large language models to classify climate-policy documents (Part II).
Estimating Coal Power Plant Operation From Satellite Images with Computer Vision
Use satellite imagery and computer vision to monitor activity at coal-fired power plants for emissions-related monitoring and reporting.
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
Use the CityLearn environment and real-world building data to design and test energy-flexibility control experiments, including reinforcement learning.
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.
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.
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.