ORCID
- Matthew Faith: 0000-0003-2764-3439
Abstract
Executive Summary. 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.
DOI
10.5281/zenodo.13687424
Publication Date
2024-09-05
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Burniston, S., Faith, M., Kriukov, V., Laidlaw, R., Kalashami, M., Rahman, F., Saggar, A., Svenning, A., Trotter, C., Zuo, K., Goldmann, K., & Roy, D. (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', Available at: https://doi.org/10.5281/zenodo.13687424
