This thesis will focus on an inter-disciplinary (intersection of artificial intelligence and climate change) research topic. Lake ice is an important variable to understand the regional and global climate change and has been recently recognized as an Essential Climate Variable (ECV). Monitoring and analyzing the (decreasing) trends in lake freezing using artificial intelligence techniques [1, 2] provides important information for climate research. This work is part of the project “Integrated lake ice monitoring and generation of sustainable, reliable, long time series” initiated and financed by the Federal Office of Meteorology and Climatology (Meteo Swiss) in the framework of GCOS (Global Climate Observing System), Switzerland.
The goal of this thesis is to detect the frozen parts of lake Sils (Graubuenden, Switzerland) using images (RGB and Thermal) captured using two UAVs (DJI Phantom 4 Pro, PixHawk). The major tasks are as follows:
• Pre-processing the thermal images to remove artefacts.
• Geo-referencing the data (RGB and Thermal) and 3D model generation.
• Semantic Segmentation (4 classes: water, ice, snow, clutter) using Few-Shot Learning . As a contingency plan, do the classification using Support Vector Machines/Random Forests.
• Ground truth generation for classification.
Note: Intermediate programming skills required in Matlab/Python/C++.
Manu Tom MSc. (email@example.com)
Lake Ice Detection
Drone Image Processing
IDEA League Student Grant (IDL)
CLS Student Project (MPG ETH CLS)
Information, Computing and Communication Sciences
Engineering and Technology