Introduction

Showcasing the TAIAO Project: Providing Resources for Machine Learning from Images of New Zealand’s Natural Environment presents an overview of the TAIAO initiative and its efforts to build high‑quality, openly accessible resources for machine learning on environmental image data. The project focuses on enabling researchers, conservationists, and practitioners to develop AI systems capable of recognising species, monitoring ecosystems, and supporting biodiversity efforts across Aotearoa New Zealand. By assembling large, curated datasets and developing tools tailored to ecological imagery, the TAIAO project aims to accelerate progress in environmental machine learning.

Outcomes

The article highlights the creation of extensive image datasets representing New Zealand’s flora, fauna, and natural environments, along with the development of machine‑learning tools and benchmarks designed specifically for ecological applications. It outlines how these resources support tasks such as species identification, habitat classification, and environmental monitoring. The authors also demonstrate how TAIAO’s datasets and software pipelines can be used to train and evaluate modern machine‑learning models, lowering barriers for researchers and practitioners working in conservation technology. Overall, the work showcases TAIAO’s contribution to building a national‑scale foundation for environmental AI research.

This article features contributions from Nick Lim, Albert Bifet, Eibe Frank, Bernhard Pfahringer, and Geoff Holmes. 

Bifet, Albert, et al. “Showcasing the TAIAO Project: Providing Resources for Machine Learning from Images of New Zealand’s Natural Environment.” Communications in Computer and Information Science, vol. 1343, 2021, pp. 3–14.