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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

baspy icon baspy

Making it far easier to read in and work with large volumes of climate model output from CMIP5/6

environmental_intelligence icon environmental_intelligence

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

flood_risk_shipston icon flood_risk_shipston

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.

geograph icon geograph

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.

hurricane-harm-herald icon hurricane-harm-herald

A python codebase to predict building damages after hurricanes. Project from the AI4ER CDT at the University of Cambridge.

oceans-gtc icon oceans-gtc

GTC 2024: Predicting Abyssal MOC Strength with Satellite-Observable Variables

resources icon resources

The default repo for wiki and issues relating to the AI4ER CDT

seaiceextent icon seaiceextent

AI4EO GTC 2021/2. Private repository for group 2: detecting sea ice extent in visible/ SAR imagery.

weather-fun icon weather-fun

An example project for the revision control training course

wildfiredistribution icon wildfiredistribution

AI4EO GTC 2021/2. Private repository for group 1: determining wildfire distribution in visible remote sensing imagery.

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