Thesis Code: 20009
Thesis Type: M.Sc. thesis in Machine Learning, Data Science, Computer Science, Mathematics, or equivalent
Research Area: Data Science for Industrial and Societal Application
Requirements:
- Knowledge of Python
- Software development skills
- Basic concepts on data science, concerning data analysis, processing and machine learning
- Basic concepts on image processing
Description:
Historical architecture is an important part of Italy’s cultural heritage, so good maintenance of these buildings is crucial for their preservation. Infrared thermography offers a method of visualization that is nondestructive and capable of revealing various types of archaeological anomaly. It can be detected the presence of moisture due to condensation or capillary rise, that can damage the plaster or fresco. Also it can be detected the presence of mold below the surface. The thermal imaging camera can also be used to check the state of adhesion between plaster and the underlying structure or to detect hidden cracks and the presence of infill, or spot previous renovations and hidden structures, but also detect damage caused by an earthquake. With the information gained from thermographic surveys using a thermal imaging camera the preservation of these highlights of Italian culture is ensured.
The objective of this thesis consists in the study and implementation of machine learning algorithms useful for recognizing and report archaeological anomalies highlighted by thermal cameras. The proposed algorithms will be trained using a synthetic dataset and open datasets. The candidate will have both the task of creating the dataset and studying and evaluating the best algorithms to apply for the case of study.
The candidate is required to implement machine learning algorithms, with the exploratory possibility of deep learning algorithms using popular framework (TensorFlow, PyTorch, Keras, etc..).
Contact: send a resume with attached the list of exams to mirko.zaffaroni@linksfoundation.com specifying the thesis code and title.
