Luca Zelioli
PhD
luca.l.zelioli@utu.fi |
deep learning, artificial intelligence, soil type prediction, software engineering
Luca Zelioli received B.S. degree from the Turku University of Applied Sciences (2018) and M.S. degree from the Åbo Akademi (2020). His M.S. thesis topic was “Environmental damage assessment based on satellite imagery using machine learning”.
He has been working as a Doctoral Candidate / Researcher in the Department of Computing, University of Turku since April 2021. In September 2024 he has been awarded the degree of Doctor of Philosophy in the Field of Natural Sciences. The degree was completed in Computer Science. He has written a doctoral dissertation: "Leveraging machine learning for maritime object detection and peatland classification: harnessing the power of machine learning for precise maritime object detection and peatland classification".
Luca continues to work in the Department of Computing as a Senior Researcher.
His main research interests include Sensor Fusion, Artificial Intelligence and Software Engineering.
Teacher assistant in Deep Learning course
- GeoFusion: A hybrid convolutional neural network-transformer model for boreal peatland classification (2026)
- Engineering Applications of Artificial Intelligence
- Image partitioning with windowed and panoramic configuration for passive 360-degree camera in military unmanned ground vehicle: A machine learning-based detection framework (2026)
- Journal of Military Studies
- A Comparative Study of a Real-Time Multi-Person Tracking System in an Urban Square Dataset (2025) 2025 International Conference on Activity and Behavior Computing (ABC) Jafarzadeh, Pouya; Zelioli, Luca; Farahnakian, Fahimeh; Nevalainen, Paavo; Heikkonen, Jukka
- Deep Mix: AI in Littoral Sonar Operations (2025)
- Journal of marine science and application
- Enhancing hurdles athletes’ performance analysis: A comparative study of cnn-based pose estimation frameworks (2025)
- Multimedia Tools and Applications
- Peatland pixel-level classification via multispectral, multiresolution and multisensor data using convolutional neural network (2025)
- Ecological Informatics
- Addressing imbalanced data for machine learning based mineral prospectivity mapping (2024)
- Ore Geology Reviews
- Enhancing Peatland Classification using Sentinel-1 and Sentinel-2 Fusion with Encoder-Decoder Architecture (2024) 2024 27th International Conference on Information Fusion (FUSION) Zelioli, Luca; Farahnakian, Fahimeh; Farahnakian, Farshad; Middleton, Maarit; Heikkonen, Jukka
- Leveraging machine learning for maritime object detection and peatland classification : harnessing the power of machine learning for precise maritime object detection and peatland classification (2024) Zelioli, Luca
- SEDA: Similarity-Enhanced Data Augmentation for Imbalanced Learning (2024)
- Lecture Notes in Computer Science
