Väitös (tietojenkäsittelytiede): DI Luca Zelioli
Aika
6.9.2024 klo 12.00 - 16.00
DI Luca Zelioli esittää väitöskirjansa ”Leveraging Machine Learning for Maritime Object Detection and Peatland Classification” julkisesti tarkastettavaksi Turun yliopistossa perjantaina 6.9.2024 klo 12 (XXII Auditorio, Agora, Vesilinnantie 3, 20500 Turku).
Vastaväittäjänä toimii professori Heikki Kälviäinen (LUT-yliopisto) ja kustoksena professori Jukka Heikkonen (Turun yliopisto). Tilaisuus on englanninkielinen. Väitöksen alana on tietojenkäsittelytiede.
Väitöskirja yliopiston julkaisuarkistossa: https://urn.fi/URN:NBN:fi-fe2024080663810 (kopioi linkki selaimeen).
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Tiivistelmä väitöstutkimuksesta:
Artificial intelligence is present in our daily lives, driving innovation across various sectors, enhancing decision-making processes, and automating complex tasks. This dissertation focuses on two application sectors machine learning plays a central role. The first sector is the maritime environment, it empowers situational awareness models around maritime vehicles. The second sector is remote sensing, with a particular focus on a soil-type classification approach.
The maritime studies proposed in the dissertation investigate how traditional Convolutional Neural Network (CNN) architectures perform in detecting and recognizing objects in the environment. Additionally, Transfer Learning (TL) is implemented to enhance the quality of the detection process. Furthermore, a precisely manually annotated dataset is developed, providing a solid basis for the development of efficient object detection and tracking.
The remote sensing section of deals with the development and evaluation of a methodology that that begins with Geographic Information System (GIS) data inputs and culminates in the creation of a soil-type classification map, with a particular focus on pixel-wise soil-type classification. The proposed peatland methodologies summarize the accumulation of decayed material, aiding in the planning of agricultural or urban areas.
In conclusion, this dissertation demonstrates the role of artificial intelligence in both maritime and remote sensing applications. Advanced CNN architectures and TL enhance object detection in maritime environments, while the developed GIS-based methodology for soil-type classification aids in agricultural and urban planning. These innovations highlight the transformative impact of AI in improving efficiency and decision-making across various sectors.
Vastaväittäjänä toimii professori Heikki Kälviäinen (LUT-yliopisto) ja kustoksena professori Jukka Heikkonen (Turun yliopisto). Tilaisuus on englanninkielinen. Väitöksen alana on tietojenkäsittelytiede.
Väitöskirja yliopiston julkaisuarkistossa: https://urn.fi/URN:NBN:fi-fe2024080663810 (kopioi linkki selaimeen).
***
Tiivistelmä väitöstutkimuksesta:
Artificial intelligence is present in our daily lives, driving innovation across various sectors, enhancing decision-making processes, and automating complex tasks. This dissertation focuses on two application sectors machine learning plays a central role. The first sector is the maritime environment, it empowers situational awareness models around maritime vehicles. The second sector is remote sensing, with a particular focus on a soil-type classification approach.
The maritime studies proposed in the dissertation investigate how traditional Convolutional Neural Network (CNN) architectures perform in detecting and recognizing objects in the environment. Additionally, Transfer Learning (TL) is implemented to enhance the quality of the detection process. Furthermore, a precisely manually annotated dataset is developed, providing a solid basis for the development of efficient object detection and tracking.
The remote sensing section of deals with the development and evaluation of a methodology that that begins with Geographic Information System (GIS) data inputs and culminates in the creation of a soil-type classification map, with a particular focus on pixel-wise soil-type classification. The proposed peatland methodologies summarize the accumulation of decayed material, aiding in the planning of agricultural or urban areas.
In conclusion, this dissertation demonstrates the role of artificial intelligence in both maritime and remote sensing applications. Advanced CNN architectures and TL enhance object detection in maritime environments, while the developed GIS-based methodology for soil-type classification aids in agricultural and urban planning. These innovations highlight the transformative impact of AI in improving efficiency and decision-making across various sectors.
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