APPLYING DEEP LEARNING to CARTOGRAPHY

Student: Kamenev Vladimir

Faculty: St. Petersburg School of Physics, Mathematics, and Computer Science

Educational Programme: Machine Learning and Data Analysis (Master)

Year of Graduation: 2024

Data quality plays one of the key roles in training a stable and high-quality algorithm in machine learning tasks and in particular building entity recognition (semantic segmentation) and building polygonization. To this end, this diploma work investigates an approach of preprocessing low-resolution data with a pre-trained model to improve spatial resolution. For this purpose, data of different nature were collected and preprocessed from different sources, a model for resolution enhancement was trained, and semantic segmentation and polygonization models were also trained and evaluated on the original and new data.

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