PhD Studentship - Machine Learning for mapping historic Hedgerow loss Ref

Proposed research

Building on the principal of research previously commissioned by the National Trust to use machine learning to map historic orchards, is it possible to establish the historic extent of hedgerows in the UK in the early 20th century by applying machine learning approaches to historic mapping? How might these approaches be augmented by applying machine learning to other datasets (such as lidar), or combining the results with existing spatial data (such as biological survey records or HLC)? How can this greater understanding of hedgerow loss since the start of the 20th century inform our approaches to hedgerow restoration?

The proposed research will examine two ‘epochs’ of OS mapping, including from the late 19th or early 20th century, and from the mid-20th century, helping us to piece together hedgerow loss over time, until the first Countryside Survey data from 1978, and modern mapping of hedgerows.  These ‘timeslices’ of hedgerow mapping will help the National Trust to consider how hedgerow survival varied over time, as well as how it may have varied across different regions and countries.  What can hedgerow survival at different time periods and in different places tell us about habitat connectivity and biodiversity in the past?

The proposed Research will run alongside the National Trust’s annual ‘Blossom’ campaign, providing a huge potential audience for dissemination and engagement as the project progresses. This will also support opportunities to embed citizen science approaches alongside the use of machine learning, working with the crowd to verify and enrich results, including for instance in estimating hedgerow age through ‘Hoopers Rule’.

About the UKRI Centre for Doctoral Training in Environmental Intelligence

Our changing environment presents a series of inter-related challenges that will affect everyone’s future health, safety and prosperity. Environmental Intelligence (EI) is the integration of environmental and sustainability research with data science, artificial intelligence and cutting-edge digital technologies to provide the meaningful insight to address these challenges and mitigate the effects of environmental change.

One of the 16 UKRI AI CDTs launched in 2019, the CDT in Environmental Intelligence provides an interdisciplinary training programme for students covering the range of skills required to become a leader in EI:

• the computational skills required to analyse data from a wide variety of sources;
• expertise in environmental challenges;
• an understanding of the governance, ethics and the potential societal impacts of collecting, mining, sharing and interpreting data, together with the ability to communicate and engage with a diverse range of stakeholders.

The CDT cohort works and learns together, bringing knowledge, skills, and interests from a range of academic disciplines relevant to EI.  CDT students undertake training and professional development as a cohort, and regularly participate in seminars, symposia, and partner engagement activities including the annual CDT Environmental Intelligence Grand Challenge.  As part of the research community at the University of Exeter, CDT students benefit from networking with colleagues in the Institute for Data Science and Artificial Intelligence; the Global Systems Institute; and the Environment and Sustainability Institute.

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