Master's thesis: Machine learning and Earth observation data for mapping Swedish wetlands
Across Europe, large-scale efforts are underway to restore degraded ecosystems and halt biodiversity loss. The EU Nature Restoration Regulation places new requirements on member states to monitor and report on the condition and restoration of various habitats, including wetlands, grasslands, forests, and other ecosystems. Wetlands are among the most valuable ecosystems for biodiversity, carbon storage, water regulation, and climate adaptation, but many have been degraded through historical land use changes. At the same time, rapid advances in Earth observation and machine learning (ML) create new opportunities for large-scale, cost-effective environmental monitoring. By combining satellite imagery, aerial data, and modern ML methods, it is increasingly possible to map and monitor habitat types and ecosystem conditions with high spatial detail.
In this master thesis
, you will develop and train machine learning (ML) models for mapping Swedish wetlands, based on satellite and aerial imagery for Sweden. The work will include processing multimodal satellite images (e.g. high-resolution aerial orthophotos, DEM, Sentinel-1 and -2 data) and applying novel deep learning methods to perform fine-grained mapping of Swedish wetlands.
Techniques and models will include computer vision models (e.g., convolutional neural networks, vision transformers), as well as various data-efficiency techniques within ML. You will work in a multi-disciplinary team which includes both ML and biodiversity experts from RISE and the Swedish Environmental Protection Agency (Naturvårdsverket; project stakeholder).
The work requires students with excellent skills within ML, image processing, and preferably also remote sensing and/or GIS. As this is a master thesis project with a research organization, we will help you reach a high level of research excellence, and a successful project will ideally result in writing a joint research paper in addition to the master thesis.
Related reading:
EU Nature Restoration Regulations: https://environment.ec.europa.eu/topics/nature-and-biodiversity/nature-restoration-regulation_en
The Swedish Environmental Protection Agency about nature restoration (Swedish): https://www.naturvardsverket.se/amnesomraden/mark-och-vattenanvandning/eu-forordning-for-att-restaurera-natur/
Required skills:
Experience of implementing machine learning, especially deep learning, models.
Courses in machine learning, image analysis, or similar.
Programming skills. Preferably with some experience of relevant frameworks such as Pytorch or JAX.
Preferred skills:
Courses in GIS, remote sensing, or similar.
Experience with geospatial data processing, e.g., QGIS, GDAL, Geopandas, Rasterio.
Supervisors:
Aleksis Pirinen (
[email protected]) and Isabelle Tingzon (
[email protected])
Start date:
Spring 2027
Location:
Lund
Credits:
30 ECTS
RISE
is Sweden’s research institute. Through our international collaboration programmes with industry, academia and the public sector, we ensure the competitiveness of the Swedish business community on an international level and contribute to a sustainable society. Our 3,300 employees engage in and support all types of innovation processes.
Welcome with your application!
If you have questions, please contact
[email protected]. We will interview suitable candidates as applications are received. Please send in your application as soon as possible. The
last day of application is 30th of October
. Note that all applications for this position must go through our recruitment system. We do
not
accept applications by e-mail.