(he/him)
Growing up on a farm in rural Rustenburg, South Africa, with the Magaliesburg mountains as his backyard, Oliver developed a deep connection with the natural world from an early age. This curiosity grew into a lifelong passion, leading him to pursue a BSc in Environmental Sciences at Nelson Mandela University, majoring in Zoology and Botany. During his studies, Oliver discovered a love for research and the wide-ranging knowledge that natural sciences offer. This diversity is reflected in his work, from studying the foraging behaviour of endangered Cape gannets using video data for his BSc Hons. to investigating the thermal tolerance of small mammals under extreme heat stress for his MSc and now looking how best to leverage AI to increase the efficiency of biodiversity monitoring projects.
Beyond his academic pursuits, Oliver embraced opportunities to share knowledge and turn personal challenges into ways to help others. While completing his MSc, he lectured in applied human and animal anatomy and physiology at Nelson Mandela University and trained as a data analyst with Masterschools. During this time, Oliver was diagnosed with ADHD, which required him to develop strategies to navigate academic life effectively. This experience inspired him to train as an ADHD and life skills coach, motivated by a desire to support others facing similar challenges.
When not immersed in research or teaching, Oliver enjoys exploring the outdoors, sipping coffee at local cafés, or discovering new running trails. Driven by curiosity and a love for learning, he is passionate about making meaningful contributions to understanding and preserving the natural world.
Developing an efficient and accessible “data-to-decision” pipeline for remotely sensed biodiversity monitoring data
PI and Institution:
Chris Sutherland, University of St Andrews
PhD aim:
To develop a more user friendly, statistically rigorous and effective “data-to-decision” pipeline for remotely sensed biodiversity monitoring data by leveraging advances in AI, image, and sound recognition.
PhD objectives:
- Develop statistical methods geared towards processing data outputs from automated image processing.
- Quantify the difference in accuracy and performance between automated and manual image detection across different detection probability thresholds.
- Investigate the potential for fully automated remote sensing systems to generate live data detection and data outputs using 4G enabled camera traps.
- Reduce the skills gap needed to interpret and process image-recognition data by refining the current Conservation AI platform.
Contact details:
Email: [email protected]
ResearchGate: Oliver Hartley
LinkedIn: Oliver Hartley
BlueSky: @ohartley.bsky.social
Conferences:
2021 International Congress of Zoology presenting Quantifying the effect of wet-bulb temperature on the rate of heat storage in Rhabdomys pumilio
Publications:
Fergus et al (2024) – Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data. Sensors. 24 (24): 8122