Master's thesis: Robust and uncertainty-aware urban land-cover mapping at property scale

IVL SWEDISH ENVIRONMENTAL RESEARCH INSTITUTE IS AN INDEPENDENT RESEARCH INSTITUTE WITH SWEDEN’S BROADEST ENVIRONMENTAL PROFILE.

TOGETHER WITH INDUSTRY, PUBLIC AGENCIES, AND THE RESEARCH COMMUNITY, WE DRIVE THE TRANSITION TO A SUSTAINABLE SOCIETY, FROM

SCIENCE TO REAL-WORLD IMPACT. WE OPERATE ACROSS THE FULL ENVIRONMENTAL AND SUSTAINABILITY SPECTRUM, AND OUR ACTIVITIES ARE

ORGANIZED INTO SEVERAL LARGER AREAS THAT BRING TOGETHER RELATED EXPERTISE AND PERSPECTIVES.

We're currently looking for a master's student who would like to carry out their thesis project with us. This is an opportunity to

work on a real-world research challenge, gain hands-on experience in applied environmental research, and collaborate with experts

in the field. By doing your master's thesis at IVL, you'll gain unique insights into our research and contribute to solutions that

support a more sustainable society.

Cities and property owners need fast, reliable facts about their sites to plan for climate adaptation, biodiversity and water

management. Krontech is a research project where a practical tool is being built that turns aerial images and GIS data into clear,

property-level numbers—such as how much area is green or paved, tree-canopy cover and where improvements would have the greatest

effect.

We already combine orthophotos, laser-scanned height models (LiDAR), NDVI and terrain data. We are also training AI segmentation

models to produce detailed land-cover maps. This thesis will build on the existing models, data and mapping workflow.

– Investigate how seasons, shadows and tree canopies affect land-cover segmentation and develop methods that make the existing

approach more robust. Possible approaches include multi-season imagery, shadow-aware training, additional GIS or LiDAR data, and

uncertainty estimation for ground cover beneath trees.

– Develop a more detailed classification of hard surfaces, such as asphalt, paving stones, gravel and compacted soil. Evaluate the

impact of this additional detail and its uncertainty on one or more environmental indicators, such as indicators related to urban

heat, permeability or water runoff.

Experience with computer vision, semantic segmentation, GIS, remote sensing, PyTorch, QGIS, GeoPandas, GDAL or rasterio is

beneficial.

The thesis will be carried out in collaboration with IVL Swedish Environmental Research Institute and the Krontech project

partners. The final direction and scope will be agreed upon with the student based on their interests, available data and academic

requirements.