Summer Student: Geospatial Deep Learning for Forestry Applications - Scion

Scion

Rotorua, Bay of Plenty
Contract
Full-time

14 days ago

Summer Student: Geospatial Deep Learning for Forestry ApplicationsBased in RotoruaApproximate dates: 1 December 2025 - 20 February 2026As part of the Government's reset of the science, innovation and technology system, on 1 July 2025, AgResearch, Manaaki Whenua - Landcare Research, Plant & Food Research, and Scion merged to form the New Zealand Institute for Bioeconomy Science, trading as the Bioeconomy Science Institute.This merger has created a world-class research organisation of globally significant size and scale, with over 2,000 employees - including scientists, researchers, and support staff. It brings together fundamental science knowledge and expertise in the natural resources that underpin the bioeconomy and the native estate, alongside applied research capabilities in manufacturing, agritech and biotech, and the food and fibre sectors.Our people drive innovation and commercial outcomes in the bioeconomy, using research and technology to support enduring economic growth and resilience, a healthy environment, and positive social outcomes for Aotearoa New ZealandThis position sits under the current Scion group of the Bioeconomy Science Institute.Scion specialises in research, science and technology development for the forestry industry, wood products and wood-derived materials. We lead new technology development for renewables, bioproducts and energy and the establishment of a broader-based circular bioeconomy.Scion hosts a number of summer students, each with differing specialities, from tertiary institutes across New Zealand at both our Rotorua and Christchurch Campus. The programme involves a student completing a 12-week, full-time, research project under the direction of Scion supervisors (allowing time for holidays over the Christmas and New Year). Students are able to gain hands on, paid, work experience in the field of which they are studying, where they get to work shoulder to shoulder with Scion's internationally recognised staffWe are seeking a motivated student to work on the Digital Twin for Planted Forest and CHM depth estimation projectsThe primary objective of the work/project:The student will help researchers to train state-of-the art models that combine aerial imagery and LiDAR datasets to create a prototype 'digital twin' of New Zealand's planted forests.Research AbstractThe student will contribute to national-scale geospatial research projects at Scion focused on developing innovative systems for detecting, mapping, and characterising New Zealand's planted forests. These projects combine high-resolution remote sensing data, such as aerial photography, and LiDAR, with deep learning techniques to build scalable tools for forest monitoring and analysis.The output of this project will help to monitor the extensive afforestation New Zealand will undertake to meet our climate change obligations in the coming decades.Working with large-scale public datasets, the student will assist in extracting, interpreting, and curating data, and will help evaluate model performance across multiple development iterations. This hands-on experience will provide valuable insight into the application of AI and geospatial technologies in environmental research.What the student will do

Extract geospatial and remote sensing data from various sources under supervision
Interpret aerial, and LiDAR imagery to support data preparation and validation tasks
Create high-quality labelled datasets for training and evaluating deep learning models
Work under supervision to assist with development of machine learning models
Inspect and provide feedback on model predictions

This project will expose the student to a broad range of tasks in GIS, remote sensing, and environmental data science, while also deepening their understanding of forestry applications. They will have the opportunity to develop or enhance practical skills in geospatial analysis, programming, and cloud-based data processing, as well as gain hands-on experience with cutting-edge AI tools and methods.Qualifications, skills and attributes needed:

Basic experience with GIS software such as QGIS, ArcGIS, or similar tools.
Experience in coding and modelling in Python.
Familiarity with file organisation, naming conventions, and working with structured data formats (e.g., shapefiles, GeoTIFFs)
Enthusiasm for landscape-level data, forest monitoring.
Ability to interpret complex imagery with strong attention to detail
Clear documentation of work and openness to feedback
Previous experience working with remote sensing imagery, deep learning algorithms, or machine learning training data is desirable but not essential
Ability to carry out tasks under supervision to a high level of accuracy
Ability to solve problems in a timely fashion

Applications close - 12 September 2025Job DetailsCrown research institute Scion
Reference # 59196
Posted on 22 Aug 2025
Closes on
Location(s) Rotorua
Expertise Bioinformatics, Modelling & Simulation, Statistics
Job level(s) Student
Work type(s) Fixed term
More details (document)

Scion

Apply Now