Name: AI for Environmental Risk
Type: Organization
Bio: UKRI Centre for Doctoral Training in the Application of AI to the study of Environmental Risks, University of Cambridge and British Antarctic Survey
Twitter: AI4ER_CDT
Location: Cambridge, UK
Blog: ai4er-cdt.esc.cam.ac.uk/
AI for Environmental Risk's Projects
A quick way to instantiate projects for AI4ER
Making it far easier to read in and work with large volumes of climate model output from CMIP5/6
Codebase for the 2023 GTC Project on Earthquake Predictability
Data for Environmental Intelligence: A mega list of Earth System Datasets covering earth observations, climate, water, forests, biodiversity, ecology, protected areas, natural hazards, marine and the tracking of UN's Sustainable Development Goals
The purpose of this project is to investigate whether we can establish the effectiveness of natural flood management (NFM) interventions undertaken in the British town of Shipston-on-Stour during 2017 to 2020 from publicly available meteorological data and private data from the river gauge in Shipston.
This is the repo for the GeoDataViz Workshop run by the AI4ER CDT
GeoGraph provides a tool for analysing habitat fragmentation and related problems in landscape ecology. GeoGraph builds a geospatially referenced graph from land cover or field survey data and enables graph-based landscape ecology analysis as well as interactive visualizations.
An introduction to GitHub for new AI4ER students
Guided Team Challenge 2021: Exposure Team Project
A python codebase to predict building damages after hurricanes. Project from the AI4ER CDT at the University of Cambridge.
GTC 2024: Predicting Abyssal MOC Strength with Satellite-Observable Variables
The default repo for wiki and issues relating to the AI4ER CDT
Repo for the 2022 GTC project on Sea Ice Classification
AI4EO GTC 2021/2. Private repository for group 2: detecting sea ice extent in visible/ SAR imagery.
A fun "art" project for practising with GitHub
BAS internal
An example project for the revision control training course
AI4EO GTC 2021/2. Private repository for group 1: determining wildfire distribution in visible remote sensing imagery.