Master's Thesis: Segmentation of satellite imagery with minimal supervision
The rise of unsupervised and semi-supervised learning-based models present new opportunities in the segmentation of images belonging to domains with limited availability of annotated data. This project seeks to explore how these kinds of models can be employed to identify and outline the main kinds of infrastructure observed in complex and noisy satellite images of urban and suburban areas. Furthermore, the potential advantages of utilizing map data to assist the segmentation process are to be assessed.
- Department
- ML Development
- Locations
- Göteborg
- Remote status
- Hybrid
Colleagues
Göteborg
What we value.
We’re all about collaboration. That’s because we’re solving a puzzle that cannot be completed alone. It takes trust, courage, creativity and transparency to deliver on our promise. Our team buys into this, so our customers buy into it. It’s just our way of staying on the same page.
About Repli5
We enable the development of autonomous vehicles with synthetic data. Our software reduces the cost and time required to train autonomous vehicles.
Master's Thesis: Segmentation of satellite imagery with minimal supervision
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