Luca Zelioli
PhD
luca.l.zelioli@utu.fi ORCID identifier: https://orcid.org/0000-0003-3680-5874 |
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
- Enhancing hurdles athletes’ performance analysis: A comparative study of cnn-based pose estimation frameworks (2025)
- Multimedia Tools and Applications
(A1 Refereed original research article in a scientific journal) - Addressing imbalanced data for machine learning based mineral prospectivity mapping (2024)
- Ore Geology Reviews
(A2 Refereed review article in a scientific journal ) - 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
(A4 Refereed article in a conference publication ) - 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
(G5 Article dissertation ) - SEDA: Similarity-Enhanced Data Augmentation for Imbalanced Learning (2024)
- Lecture Notes in Computer Science
(A4 Refereed article in a conference publication ) - 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
(A4 Refereed article in a conference publication ) - Enhancing Minerals Prospects Mapping with Machine Learning: Addressing Imbalanced Geophysical Datasets and Data Visualization Approaches (2023)
- Proceedings of Conference of Open Innovations Association FRUCT
(A4 Refereed article in a conference publication ) - 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
(A4 Refereed article in a conference publication ) - 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
(A4 Refereed article in a conference publication ) - ABOships-An Inshore and Offshore Maritime Vessel Detection Dataset with Precise Annotations (2021)
- Remote Sensing
(A1 Refereed original research article in a scientific journal) - Transfer Learning for Maritime Vessel Detection using Deep Neural Networks (2021)
- Proceedings of the IEEE international conference on intelligent transportation systems
(A4 Refereed article in a conference publication ) - COMPARING CNN-BASED OBJECT DETECTORS ON TWO NOVEL MARITIME DATASETS (2020)
- IEEE International Conference on Multimedia and Expo Workshops
(A1 Refereed original research article in a scientific journal) - Environmental damage assessment based on satellite imagery using machine
learning (2020) Zelioli Luca
(G2 Master’s thesis, polytechnic Master’s thesis)
