TY - BOOK
T1 - Data Study Group Final Report: UK Centre for Ecology & Hydrology (UKCEH) - Advancing Insect Biodiversity Monitoring through Automated Sensors, Deep Learning, and Citizen Science Data
AU - Burniston, Sonny
AU - Faith, Matthew
AU - Kriukov, Vitalii
AU - Laidlaw, Rachael J.
AU - Kalashami, Mahsa P.
AU - Rahman, Farzana
AU - Saggar, Arpita
AU - Svenning, Asger
AU - Trotter, Cameron
AU - Zuo, Kaiwen
AU - Goldmann, Katriona
AU - Roy, David
PY - 2024/9/5
Y1 - 2024/9/5
N2 - Data Study Groups are week-long events at The Alan Turing Institute bringing together some of the country’s top talent from data science, artificial intelligence, and wider fields, to analyse real-world data science challenges.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.
AB - Data Study Groups are week-long events at The Alan Turing Institute bringing together some of the country’s top talent from data science, artificial intelligence, and wider fields, to analyse real-world data science challenges.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.
UR - https://zenodo.org/doi/10.5281/zenodo.13687423
U2 - 10.5281/ZENODO.13687423
DO - 10.5281/ZENODO.13687423
M3 - Commissioned report
BT - Data Study Group Final Report: UK Centre for Ecology & Hydrology (UKCEH) - Advancing Insect Biodiversity Monitoring through Automated Sensors, Deep Learning, and Citizen Science Data
ER -