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. About the Master's Thesis 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. Possible thesis directions The thesis could explore one of the following directions or a related topic developed together with the student. A. Robust land-cover mapping under challenging conditions – 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. Possible research questions How do season, shadow and tree cover affect segmentation accuracy? Can multi-season or complementary data improve model robustness? How can uncertainty be estimated for partly or completely hidden surfaces? B. Detailed hard-surface mapping and environmental applications – 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. Possible research questions Which hard-surface types can be reliably distinguished? What data and annotation strategy are needed? How does a more detailed classification affect selected environmental indicators? How do classification accuracy and uncertainty affect the reliability of these indicators? Data you will use The available data includes: High-resolution orthophotos from multiple years Existing land-cover annotations and model predictions LiDAR-derived elevation and canopy information NDVI and terrain data Building, road, property and other GIS layers Access to individual datasets is subject to applicable licences and project agreements. Qualifications The ideal candidate has experience in: programming in Python training and using neural networks Experience with computer vision, semantic segmentation, GIS, remote sensing, PyTorch, QGIS, GeoPandas, GDAL or rasterio is beneficial. Supervision & collaboration 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. Host Organization IVL Swedish Environmental Research Institute Location: Stockholm/Gothenburg/Malmö Credits: 30 ECTS Group size: 1–2 students Start date: Flexible Academic supervisor/examiner: arranged with your university Interested? We'd love to hear from you. Submit your application by clicking "Register your interest".