CapyMOA
We are New Zealand data and environmental scientists using artificial intelligence to solve some of the biggest environmental issues facing New Zealand.
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Latest News
TAIAO Data and Expertise Help Power Global Finalist Recognition in the World Bank Climate Risk Challenge
August 26, 2025
A project drawing on the TAIAO programme has won international recognition, with the team DelphiQ named grand finalists of the World Bank Climate Risk Challenge, part of the 2025 Global Industry Challenge (GIC). After successfully advancing through all three phases of the competition, the team has b...
Hackathon 2025
The 2025 AI Hackathon was a big success!
This year's Waikato Hackathon event saw 17 participants across four teams developing a range of innovative AI solutions to real world environmental problems.
Team Gallagher were the winning team with their project Recraft AI: Turning Commercial Waste into Community Treasure which proposed a system to reduce and upcycle construction waste through community-led initiatives.
August e-newsletter
In this newsletter you will find:
Information about the launch of our new website
The latest news on the upcoming Artificial Intelligence for Earth and Environmental Science workshop and AI Hackathon
Other upcoming events
Highlights from the Environmental Data Science and AI Summit
On Monday, 19 August TAIAO 2024 hosted its first-ever Environmental Data Science and AI Summit at Victoria University of Wellington.
An initiative supported by the Ministry of Business, Innovation and Employment (MBIE), TAIAO is committed to promoting and facilitating the adoption of artificial intelligence (AI) and data science in New Zealand's environmental sector.
Recognising that good data is essential to research, understand, and set policy for effective management of our natural environment, the team at TAIAO are working to develop new machine learning methods that are tailored to process environmental data gathered from our New Zealand context.
TAIAO uses AI models to analyse vast amounts of historical and real-time data, and identify patterns to predict future trends, such as extreme weather events. Through this mahi, we aim to break barriers by enhancing industry capability, and pioneer new machine learning methods to process vast amounts of data in real-time to meet today's pressing demands.
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The University of Waikato
University of Canterbury
The University of Auckland
Victoria University of Wellington
MetService
Beca
