We are delighted to announce that our PhD student, Ben Bartlett, has successfully defended his thesis on Advancing Ecological Survey through Automation, Artificial Intelligence, and Unmanned Aerial Systems. His groundbreaking research presents a real-time, adaptive UAV system designed to revolutionize ecological surveys by addressing the challenge of balancing wide-area coverage with high-resolution data collection.
At the heart of his work is the Modular Detection and Targeting System (MDTS), a cutting-edge technology that integrates thermal imaging for broad detection and high-resolution RGB zoom for precise species identification. This system enables rapid, efficient wildlife monitoring while significantly reducing redundant data collection. Field trials demonstrated its effectiveness, achieving over 300-fold improvements in image resolution and up to a 1000-fold reduction in data volume compared to traditional UAV methods.
The MDTS’s modular design allows it to be adapted for various ecological applications, from wildlife conservation to environmental impact assessments. By providing classification-ready data with minimal post-processing, this innovation bridges the gap between detection and actionable ecological insights.
Ben’s research exemplifies how artificial intelligence and automation can enhance environmental science, offering scalable solutions for the future of ecological monitoring. We congratulate Ben on this outstanding achievement and look forward to seeing the impact of his work in the field of ecological research.
We are delighted to announce that our PhD student, Ben Bartlett, has successfully defended his thesis on Advancing Ecological Survey through Automation, Artificial Intelligence, and Unmanned Aerial Systems. His research presents a real-time, adaptive UAV system designed to revolutionize ecological surveys by addressing the challenge of balancing wide-area coverage with high-resolution data collection.
At the heart of his work is the Modular Detection and Targeting System (MDTS), a cutting-edge technology that integrates thermal imaging for broad detection and high-resolution RGB zoom for precise species identification. This system enables rapid, efficient wildlife monitoring while significantly reducing redundant data collection. Field trials demonstrated its effectiveness, achieving over 300-fold improvements in image resolution and up to a 1000-fold reduction in data volume compared to traditional UAV methods.
The MDTS’s modular design allows it to be adapted for various ecological applications, from wildlife conservation to environmental impact assessments. By providing classification-ready data with minimal post-processing, this innovation bridges the gap between detection and actionable ecological insights.
Ben’s research exemplifies how artificial intelligence and automation can enhance environmental science, offering scalable solutions for the future of ecological monitoring. We congratulate Ben and his supervisors, Prof Gerard Dooly, Prof Petar Trslic and Dr Matheus Santos on this outstanding achievement and look forward to seeing the impact of his work in the field of ecological research.
