AI Vision Sensor Networks for Monitoring Avian Activities at Wind Farms

The Challenge

Wind energy is a critical component of New South Wales’ transition to Net Zero emissions, yet wind farms continue to face community concern and regulatory scrutiny regarding their impact on bird populations. Current approaches for measuring bird mortality at wind farms rely heavily on manual carcass searches, which are often inaccurate, inconsistent, and impractical in remote or offshore environments. As a result, there is limited reliable evidence on the frequency of bird collisions, the species affected, and the broader ecological consequences of wind farm operations. This lack of robust data creates uncertainty for industry, regulators, and the public, potentially slowing the deployment of renewable energy infrastructure. The challenge is therefore to develop a reliable, scalable monitoring system capable of accurately detecting and quantifying bird activity and collision events, providing the evidence needed to better understand wildlife impacts while supporting renewable energy development.

The Solution

The project proposes an AI-enabled vision sensor network that continuously monitors bird activity around wind turbines and automatically identifies bird species, tracks flight paths, and detects potential collision events. The solution combines multiple fixed wide-angle cameras, pan-tilt-zoom cameras, edge computing, cloud-based data processing, and advanced computer vision algorithms to create a scalable wildlife monitoring platform. Building on an existing collaboration between the Dr Zihao Wang at the University of Sydney, A/Prof Cheng-Lung Wu at UNSW, and industry partner HoekSec Pty Ltd, the project will refine AI models, optimise camera placement, and validate field deployment through trials at Sapphire Wind Farm in regional NSW. The resulting system aims to generate reliable evidence on bird interactions with wind farms, supporting environmental management, informing policy decisions, and strengthening public confidence in renewable energy infrastructure.

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