Luca Zelioli
luca.l.zelioli@utu.fi |
Areas of expertise
deep learning, artificial intelligence, soil type prediction, software engineering
deep learning, artificial intelligence, soil type prediction, software engineering
Biography
Luca Zelioli received B.S. degree from the Turku University of Applied Sciences (2018) and M.S. degree from the Åbo Akademi (2020). He is currently working as Doctoral Candidate / Doctoral Student in Department of Computing, University of Turku since January 2022. Previously he was working as a Project Researcher in the same department since April 2021. His M.S. thesis topic was “Environmental damage assessment based on satellite imagery using machine learning”.
Research
His main research interests include Sensor Fusion, Artificial Intelligence and Software Engineering.
Teaching
Teacher assistant in Deep Learning course
Publications
- CNN-based Boreal Peatland Fertility Classification from Sentinel-1 and Sentinel-2 Imagery (2023) 2023 IEEE International Symposium on Robotic and Sensors Environments (ROSE) Farahnakian Fahimeh, Zelioli Luca, Middleton Maarit, Seppä Iiro, Pitkänen Timo P., Heikkonen Jukka
(Refereed article in conference proceedings (A4)) - Enhancing Minerals Prospects Mapping with Machine Learning: Addressing Imbalanced Geophysical Datasets and Data Visualization Approaches (2023)
- Proceedings of Conference of Open Innovations Association FRUCT
(Refereed article in conference proceedings (A4)) - Multistream Convolutional Neural Network Fusion for Pixel-wise Classification of Peatland (2023) 2023 26th International Conference on Information Fusion (FUSION) Farahnakian Fahimeh, Zelioli Luca, Pitkänen Timo, Pohjankukka Jonne, Middleton Maarit, Tuominen Sakari, Nevalainen Paavo, Heikkonen Jukka
(Refereed article in conference proceedings (A4)) - Real-Time Military Tank Detection Using YOLOv5 Implemented on Raspberry Pi (2023) 2023 the 4th International Conference on Artificial Intelligence, Robotics and Control (AIRC 2023) Jafarzadeh Pouya, Zelioli Luca, Farahnakian Fahimeh, Nevalainen Paavo, Heikkonen Jukka, Hemminki Petteri, Andersson Christian
(Refereed article in conference proceedings (A4)) - ABOships-An Inshore and Offshore Maritime Vessel Detection Dataset with Precise Annotations (2021)
- Remote Sensing
(Refereed journal article or data article (A1)) - Transfer Learning for Maritime Vessel Detection using Deep Neural Networks (2021)
- Proceedings of the IEEE international conference on intelligent transportation systems
(Refereed article in conference proceedings (A4)) - COMPARING CNN-BASED OBJECT DETECTORS ON TWO NOVEL MARITIME DATASETS (2020)
- IEEE International Conference on Multimedia and Expo Workshops
(Refereed journal article or data article (A1)) - Environmental damage assessment based on satellite imagery using machine
learning (2020) Zelioli Luca
(Master’s thesis, polytechnic Master’s thesis (G2))
UTU Research Portal