Smarter landâcover mapping for propertyâscale climate metrics
Smarter landâcover mapping for propertyâscale climate metrics
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Job Description
About the project
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 propertylevel numberslike how much area is green vs. paved canopy cover and where upgrades (e.g. green roofs) would help the most.
We already combine orthophotos laserscanned height models (LiDAR) NDVI and terrain (e.g. slope). We are also training AI segmentation models to make the maps sharper. A key challenge is what lies under tree canopiesgrass bare soil paths Getting this right matters for runoff cooling and habitat.
What you will do
- Train and compare segmentation models for urban land cover at high resolution.
- Fuse multiple data sources (orthophotos LiDAR/DSMDTM NDVI slope) and test what each adds.
- Use multiyear imagery to handle seasons and improve class separation.
- Tackle the underthetrees problem using canopy height crown shape and context (park vs. street).
- Turn maps into parcellevel metrics that support reporting (e.g. CSRD) and simple targets like the 330300 rule of thumb.
- (Nicetohave) Build a small web demo to visualize parcels masks and KPIs.
Possible thesis angles (pick one or mix two)
A. Undercanopy inference Predict the ground cover beneath tree crowns using fused data and simple context rules.
B. Multisource & multiseason fusion Show how extra inputs (height NDVI seasons) improve over singledate RGB.
C. From maps to metrics Turn segmentation into clear property KPIs and a first take on 330300 with basic uncertainty.
Data you will use
- Highresolution orthophotos (several years for the same sites)
- LiDARderived DSM/DTM and canopy metrics
- NDVI and terrain derivatives (slope/aspect)
- Parcel boundaries and other GIS layers (roads buildings parking)
- Optional: soil/landform and carbonstorage layers where available
(Access and licensing follow Swedish public data policies and partner agreements.)
What you bring
There are no strict requirements. It helps if you have:
- Python skills and interest in ML/computer vision or remote sensing
- Some GIS experience (QGIS/GeoPandas/GDAL/rasterio)
- Curiosity about cities climate adaptation and biodiversity
- Bonus: PyTorch/TensorFlow web mapping (Leaflet/Mapbox) basic SQL
Supervision & collaboration
- Industrial supervisor: IVL (e.g. Jens Wilhelmsson/Agnes Olsson)
- Academic supervisor/examiner: arranged with your university
- Realestate partners provide pilot data and feedback
Host Organization
IVL Swedish Environmental Research Institute (IVL) together with participating realestate partners.
Location: Stockholm/Malm/Gteborg
Credits: 30 ECTS
Group size: 12 students
Start date: Flexible (earliest January 2026)
Employment Type
Full Time
