Abstract
The overarching goal in this Alan Turing Institute Data Study Group (DSG) was to advance understanding and support conservation efforts related to insect populations and biodiversity monitoring. This was achieved through the integration of reliable and trustworthy machine learning applications, with datasets provided by the UK Centre for Ecology & Hydrology (UKCEH).
Our objectives were twofold:
- Develop advanced analytical techniques for generating biodiversity metrics and interactive data visualisations. These tools aim to promote stakeholder engagement and interest in biodiversity monitoring.
- Enhance the transparency of decision-making in machine learning models and increase the trustworthiness of subsequent biodiversity monitoring results.
Our work ultimately contributes to global biodiversity protection by providing tangible, reliable insights and a comprehensive understanding of ecosystem dynamics.
Citation information
Data Study Group Team. (2024). Data Study Group Final Report: UK Centre for Ecology & Hydrology (UKCEH) - Advancing Insect Biodiversity Monitoring through Automated Sensors, Deep Learning, and Citizen Science Data (Version 1). The Alan Turing Institute. https://doi.org/10.5281/zenodo.13687424
Additional information
Sonny Burniston, NatureMetrics
Matthew Faith, University of Plymouth
Vitalii Kriukov, Aston University
Rachael Joanne Laidlaw, University of Bristol
Mahsa Pourhossein Kalashami, University of Leicester
Farzana Rahman, Kingston University
Arpita Saggar, University of Leeds
Asger Svenning, Aarhus University
Cameron Trotter, British Antarctic Survey
Kaiwen Zuo, University of Warwick
Katriona Goldmann, The Alan Turing Institute
David Roy, UK Centre for Ecology & Hydrology