Research Fellow: 3DTreePrint Project

Department of Geography

Job Description

Research Fellow

3DTreePrint Project

Department: Geography

Reports to

Dr. Martin Mokroš

Context

University College London (UCL) is one of the world's top universities. Based in the heart of London, it is a dynamic, outward-looking institution. At its establishment in 1826, UCL was radical and responsive to the needs of society, and this ethos – that excellence should go with enriching society – continues today.

The UCL Department of Geography is a leading UK and global department, with a heritage of excellence in research, renowned for its innovative and interdisciplinary research to understanding complex global challenges. It brings together expertise

Grade: 7 spine point 32,1FTE

This is a full time, fixed term post with funding available until 31 May 2028. The appointment is available to start on the 1st of December 2026.

Location: UCL Bloomsbury hand-in-hand in physical and human geography to explore critical issues such as climate change, sustainable development, and social justice. The department fosters an interdisciplinary approach, equipping students and researchers with the skills to critically engage with the complex interactions between people, places, and environments.

The Department of Geography is committed to the values of equality, diversity and inclusion and we have an active Equality, Diversity and Inclusion Committee, and networks for BME, LGBTQ, and disabled/neurodiverse staff and students. The Department recognises the importance of familyfriendly working and we try, as far as possible, to organise meetings and events with staff responsibilities as parents and carers in mind. In 2016, the Department was awarded an Athena

SWAN Bronze award and recently gained the Silver award (October 2022) in recognition of our advancement of gender equality: representation, progression and success. Above all the Department aims to be a supportive environment for both its staff and students. More information can be found here: https://www.geog.ucl.ac.uk/equality-diversity-inclusion/

Closed-loop resilience monitoring with scalable, repeatable, low-cost 3D structure mapping –

3DTreePrint Project

This post sits within ARIA's The Resilient Climate and Ecosystems (Advanced Research + Innovation Agency) is a UK research funding agency that supports ambitious research at the edge of the possible. Its opportunity spaces identify underexplored areas where new scientific and engineering capabilities could lead to major breakthroughs. This post sits within the ARIA's The Resilient Climate and Ecosystems opportunity seed route. Opportunity seeds provide funding of up to £500,000 for individual research teams to pursue high-potential, early-stage ideas aligned with an opportunity space but outside the scope of a larger programme. The seed mechanism is intended to give research teams the flexibility to explore new technical pathways that might otherwise fall between conventional funding routes.

We are seeking a research fellow who will join the team and will work on the Dr Martin Mokroš at UCL Geography. The 3DTreePrint project pairs low-cost LiDAR with automated opportunity seed route. ARIA

3DTreePrint project led by workflows to make detailed 3D measurement of trees affordable, repeatable and scalable, building

“3D structural fingerprints” to track ecosystem resilience over time. Main partners of the project are Forest Research, Forestry England, Earthwatch Europe and Polish Academy of Sciences.

Main purpose of the job

The 3DTreePrint aims to make detailed three-dimensional measurements of trees and forest structure more affordable, repeatable and useful for ecosystem monitoring. The project is embedded in a strong set of delivery partnerships. Forest Research, Forestry England and Earthwatch Europe are engaged as partners, giving direct access to instrumented super-sites, managed woodland, national forest inventory and Tiny Forests, and a route from prototype to national practice. Regarding the scanners, we are collaborating with Janusz Bedkowski from the Polish Academy of Sciences, and the HDMapping will be the main processing algorithm for the raw data: https://github.com/MapsHD/HDMapping.

This fixed-term post is dedicated to the computational core of the project and to delivering its second and third milestones: an automated processing pipeline that turns raw low-cost scanner data into decision-grade structural metrics, a quantified account of repeatability and uncertainty across sensors, trajectories and SLAM configurations, and a field-validated, openly released 3DTreePrint stack of prototypes, protocols, code and tutorials. In different words the main purpose of the role is to co-design, build and deliver the automated processing pipeline and to establish the evidence base that shows what it can and cannot measure reliably. The post holder will take raw data from low-cost mobile laser scanning prototypes through SLAM-based trajectory estimation and registration to treeand stand-level structural metrics, working primarily with open-source software that they will adapt, extend, test and benchmark rather than use off the shelf.

The post holder will quantify repeatability and uncertainty across hardware configurations, acquisition trajectories, SLAM pipelines and ecosystems, and will convert those results into practical configuration guidance on which combinations work best for which environment and objective. In the second half of the post they will harden the pipeline into a field-validated stack, demonstrate change detection and intervention verification from repeat scans, and prepare the open release of code, documentation, tutorials and a public dataset subset. The measure of success is adoption: the

pipeline must run in the hands of partner organisations and trained non-specialists, in UK woodlands and in international campaigns, without the post holder present.

This is a full time, fixed term post with funding available until 31 st available from 1 st December 2026.

Duties and responsibilities

• Co-design and deliver the automated 3DTreePrint processing pipeline v1.0, taking raw sensor data through registered point clouds to tree- and stand-level structural metrics, including modules for co-registration, quality control, uncertainty estimation and individual tree segmentation.
• Implement, configure, tune and benchmark open-source SLAM solutions for mobile laser scanning, assessing trajectory quality, drift and registration accuracy across sensors and prototype configurations.
• Adapt, extend and integrate existing open-source point-cloud libraries into a modular, versioncontrolled and reproducible workflow, contributing improvements upstream where appropriate.
• Quantify repeatability and uncertainty of structural metrics across hardware configurations, trajectories, processing settings and ecosystems, benchmarking against reference systems and field ground-truth and reporting accuracy, bias and precision transparently.
• Produce configuration guidance recommending hardware, trajectory and SLAM combinations for each ecosystem and objective, with documented operating envelopes and failure modes.
• Prepare the open release of code, documentation, tutorials and a curated public dataset, so that a user outside the project team can complete a field-to-metrics run from the documentation alone, and contribute to milestone reporting, technical reports and publications.

This job description may be reviewed and be subject to amendment in consultation with the post holder. Job descriptions cannot be exhaustive, and so the post-holder will carry out any other duties within the scope, spirit and purpose of the job as requested by the line manager.

The post-holder will also

• Follow and actively promote the UCL Ways of Working.
• Take responsibility to carry out duties in a way that embodies UCL's environmental and social sustainability values, actively supporting objectives within the remit of their role.
• Actively follow and promote all UCL policies including Equality, policies.

May 2028. The appointment is

UCL's Sustainability Strategy, policies and Diversity and Inclusion

• Maintain awareness and observation of Fire and Health and Safety Regulations.

Further details of the department are at https://www.ucl.ac.uk/social-historical-sciences/geography Informal enquiries may be made to Dr Martin Mokroš (

)

If you have any queries regarding the application process, please contact m.mokros@ucl.ac.uk geog.research@ucl.ac.uk

Person Specification

Note to job applicant: copy and paste the following criteria into your “Statement in support of your application” and describe underneath each criterion how you meet it, giving examples. You will be scored on how you meet each criterion.

Essential Criteria

Qualifications, experience and knowledge

PhD (awarded or close to submission) in computer science, robotics, geomatics, remote sensing, forest science or a closely related discipline, with a substantial computational component Appointment at Grade 7 is dependent upon having been awarded a PhD; if this is not the case, initial appointment will be at Research Assistant Grade 6B, with payment at Grade 7 being backdated to the date of final submission of the PhD thesis.

Substantial experience of processing point-cloud data from terrestrial and/or mobile laser scanning in forest, woodland or other structurally complex vegetated environments.

Strong understanding of tree and forest structure measurement and of how uncertainty from occlusion, SLAM drift, scan geometry, terrain and vegetation density propagates into derived structural metrics.

Skills and abilities

Advanced programming skills in Python and/or C++, including the ability to read, adapt, extend, debug and test existing open-source codebases rather than only applying them as supplied.

Advanced practical skills in point-cloud processing, including registration, filtering, segmentation, individual tree extraction and derivation of tree- and stand-level structural metrics.

Experience building reproducible and automated processing workflows, using version control, environment management or containerisation, and appropriate testing practices.

Excellent documentation and communication skills, including the ability to write user-facing documentation and tutorials for non-specialist users and to work effectively with academic and external partners.

Ability to work independently, plan and prioritise own work against fixed milestone deadlines, and escalate technical or data-quality issues promptly to the project lead.

UCL Ways of Working

Commitment to and knowledge of advancing equality, Desirable Criteria Application form/Interview Application Application form/Interview Application form/Interview Application form/Interview Application form/Interview Application form/Interview Application diversity and inclusion form/Interview

Demonstrable experience with SLAM and/or robotics, including implementing, Application configuring, tuning or benchmarking SLAM pipelines for mobile mapping and form/Interview assessing trajectory and registration quality.

Experience of releasing and maintaining open-source software, including public Application repositories, packaging, licensing, issue triage and support for external users.

form/Interview

Experience with machine learning or deep learning applied to point clouds, for Application example semantic segmentation or individual tree segmentation.

form/Interview

Experience with low-cost, prototype, handheld, mobile or experimental sensing Application systems, embedded hardware, or robotics middleware such as ROS.

form/Interview

Experience of field data collection in forest or woodland environments, and of forest Application ground-truth measurements such as tree position, DBH, height or species.

form/Interview

Interest in capacity building and knowledge transfer, and willingness to travel and Application support training of local partners during international field campaigns.

form/Interview