Jukka Heikkonen
Doctor of technology
jukhei@utu.fi ORCID-tunniste: https://orcid.org/0000-0002-2468-5708 |
Data analysis; machine learning; sensor fusion data processing; autonomous systems; computer vision; pattern recognition; GIS data processing; remote sensing
Head of the algorithms and computational intelligence (ACI) research group. See current activities/projects from the Research section below
Jukka Heikkonen has been a professor of computer science of University of Turku, Finland, since 2009. His research as the head of the Algorithms and Computational Intelligent (ACI) research group is related to data analytics, machine learning and autonomous systems. Currently he is focusing on machine learning based data/sensor fusion applied for pattern recognition, situational awareness modelling of autonomous systems, and GIS data analysis in geographical applications. He has worked at top level research laboratories and Center of Excellences in Finland and international organizations (European Commission, Japan) and has led many international and national research projects. He has authored more than 150 scientific articles.
Some examples of current research activities/projects:
Artificial Intelligence based Virtual Control Room for the Arctic (AI-ARC)
1.9.2021 – 31.8.2024, H2020 project
The main objective of the AI-ARC proposal is to create an innovative, robust, efficient, and user-friendly artificial intelligence (AI) based- platform for coast and border guards, which allows traditional and VR-based interfaces to adapt to users’ preferences in terms of information management, anomaly detection, risk analyses and interoperability; in order to realize a comprehensive surveillance system that delivers powerful sensor fusion based situational awareness for decision making, and safety for all maritime actors.
Exploration Information System (EIS)
1.4.2022-31.3.2025, Horizon Europe project
EIS will develop new data analysis methods by applying artificial intelligence, machine learning, deep learning into mineral prospectivity mapping together with new geomodels and mineral systems modelling. Methods developed reduce the current high exploration costs and improve the accuracy of the targeting of the early phase exploration.
Trafficability Prediction and Route Planning for Forest Machines
1.9.2020-31.7.2024, Academy of Finland
The objective of the project is to develop novel machine learning (ML) approach for terrain trafficability prediction for forest machines based on model-data fusion and to develop efficient route planning approaches based on the trafficability forecasts.
Smart terminals (Sea3value SMARTER)
1.1.2021-28.2.2023, Business Finland
UTU is developing data analytics solutions for smart harbour operations. The objective is to add value to stakeholders by improved sharing of information, and by redesigning processes for operational optimisation, improved safety, and optimise people and cargo flow. The ship turnaround use case focuses on optimising ships visits in harbour. The truck traffic use case focuses on developing a predictive real-time traffic situational awareness model to optimise the cargo logistics to and from the port. The passenger flow use case improves the passenger flow on ropax-ports by developing models and solutions to support collective transportation, ride sharing and other mechanisms to relieve the congestion at ports.
MAATI – Soil type estimation
1.8.2021-31.7.2022, Ministry of Agriculture
This project develops machine learning methods for soil type classification for whole Finland. This includes the use of remote sensing datasets (such as Sentinel 1 and 2 satellites), up-to-date open spatial databases for peatlands and nutrient classes (swamp types), land use (forest / field / wetland / peat production) and drainage. The methods are first developed and tested in three pilot areas in Finland: 1) Keminmaa, 2) Etelä-Pohjanmaa, and 3) Itä-Suomi. The research includes the use of classical pattern recognition approaches based on predefined features and feature selection, and also deep learning approaches and their comparisons. The research is done in collaboration with Natural Resource Institute and Geological Survey of Finland.
Compressive Sensing and Machine Learning Techniques for Radar Applications
1.1.2021-31.12.2022, Finnish Defence Forces
The project investigates integration of Compressive Sensing(CS) and machine learning for radar applications improving the speed and applicability of CS techniques using deep net architectures with learned signal priors, while supporting learning in data starved regimes with CS based models. The work at the UTU is related to the detection and classification of targets in Gaussian and Non-Gaussian clutter.
Currently responsible teacher in the following courses:
TKO 3120 Machine Learning and Pattern Recognition
TKO 5328 Erikoistyö
Supervisor in graduation theses (MSc, BSc).
- Pose estimation of sow and piglets during free farrowing using deep learning (2024)
- Journal of agriculture and food research
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä ) - SEDA: Similarity-Enhanced Data Augmentation for Imbalanced Learning (2024)
- Lecture Notes in Computer Science
(A4 Vertaisarvioitu artikkeli konferenssijulkaisussa) - Short and Long Term Vessel Movement Prediction for Maritime Traffic (2024)
- Lecture Notes in Computer Science
(A4 Vertaisarvioitu artikkeli konferenssijulkaisussa) - Supporting SME companies in mapping out AI potential: a Finnish AI development case (2024)
- Journal of Technology Transfer
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä ) - TNEST: Training Sparse Neural Network for FPGA Based Edge Application (2024)
- Lecture Notes in Networks and Systems
(A4 Vertaisarvioitu artikkeli konferenssijulkaisussa) - A Comprehensive Study of Clustering-Based Techniques for Detecting Abnormal Vessel Behavior (2023)
- Remote Sensing
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä ) - 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 Vertaisarvioitu artikkeli konferenssijulkaisussa) - Deep Learning and Computer Vision in Remote Sensing (2023) Farahnakian Fahimeh, Heikkonen Jukka, Jafarzadeh Pouya
(C2 Toimitustyö tieteelliselle kokoomateokselle) - 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 Vertaisarvioitu artikkeli konferenssijulkaisussa) - Evaluation of Spatiotemporal Fetal Cardiac Imaging Using Deep Learning Techniques (2023)
- Lecture Notes in Electrical Engineering
(A4 Vertaisarvioitu artikkeli konferenssijulkaisussa) - Hybrid Capsule Network for Hyperspectral Image Unmixing and Classification (2023)
- Lecture Notes in Networks and Systems
(A4 Vertaisarvioitu artikkeli konferenssijulkaisussa) - Ice-Water Segmentation Using Deep Convolutional Neural Network-Based Fusion Approach (2023) The 28th International Conference on Automation and Computing: Digitalisation for Smart Manufacturing and Systems Sheikh Javad, Farahnakian Fahimeh, Farahnakian Farshad, Heikkonen Jukka
(A4 Vertaisarvioitu artikkeli konferenssijulkaisussa) - Intelligent Traffic Light Solution for Green and Sustainable Smart City (2023)
- Mediterranean Conference on Embedded Computing
(A4 Vertaisarvioitu artikkeli konferenssijulkaisussa) - Machine Vision and Artificial Intelligence in Robotics for Smart Factory (2023) Proceedings of 2023 IEEE International Conference on Emerging Trends in Engineering, Sciences and Technology (ICES&T) Kanth Rajeev, Heikkonen Jukka
(A4 Vertaisarvioitu artikkeli konferenssijulkaisussa) - Multi-class Pathogenic Microbes Classification by Stochastic Gradient Descent and Discriminative Fine-Tuning on Different CNN Architectures (2023)
- Lecture Notes in Networks and Systems
(A4 Vertaisarvioitu artikkeli konferenssijulkaisussa) - 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 Vertaisarvioitu artikkeli konferenssijulkaisussa) - One, Five, and Ten-Shot-Based Meta-Learning for Computationally Efficient Head Pose Estimation (2023)
- International Journal of Embedded and Real-Time Communication Systems
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä ) - 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 Vertaisarvioitu artikkeli konferenssijulkaisussa) - Sea4Value – Smart Terminals: DIMECC Final Report 1/2023 (2023) Lappalainen Anssi, Hollström Tommi, Penttinen Toni, Likhachev Roman, Vojnovski Nikola, Huttunen Heikki, Selinummi Jyrki, Liang Liang, Hakala Joona, Juvonen Konsta, Keso Elias, Lempinen Kristian, Sartori Paula, Koskinen Tomi, Sarpola Petri, Kujala Iiris, de Andrade Vincent, Aarnio Petri, Angelov Vaklin, Hakila Mikko, Jalo Sami, Kinnunen Jan, Kääriäinen Santeri, Lehtonen Eero, Poikonen Jussi, Salminen Simo, Tenovuo Karno, Virjonen Petra, Virtanen Kari, Wiik Jonatan, Heikkonen Jukka, Jafarzadeh Pouya, Penttinen Tommi, Hellström Magnus, Tsvetkova Anastasia, Wahlström Irina, Chen Yiran, Heikkilä Marikka, Edelman Kristel, Iancu Bogdan, Morariu Andrei-Raoul, Lilius Johan, Väre Jani, Borhany Navid, Podanowski Maciej, Furca Jacek, Wojas Paweł, Tokarz Maciej, Kowalik Piotr, Swiercz Jacub, Nowak Anna, Idziak Paweł, Olech Michał, Janik Krzysztof, Wach Amadeusz, Głowacki Rafał, Jerzykowski Tomasz, Puczynski Michał, Jurynec Longin, Niewiera Aleksandra, Sikorski Waldemar, Lamperski Dariusz, Baczar Dariusz, Jokela Tero, Hallio Juhani, Auranen Jani, Talmola Pekka, Kalliovaara Juha, Koskinen Juho, Arajärvi Antti, Ekqvist Jani, Vanharanta Jani, Wallin Markus, Luimula Mika, Haavisto Timo, Aho Jami, Nikkola Aapo, Näykki Alarik, Laatikainen Antti, Vu Duy, Laivuori Niko, Majala Timo, Ranta Vesa, Wendelin Heikki, Heino Joni, Meriläinen Kyösti, Meriläinen Elias, Holma Valtteri, Pakanen Marko, Wiker Tuukka, Elhadi Ahmed, Mbah Lionel, Nguyen Narinen Truc, Salokorpi Mirva, Angelov Vaklin, Sundholm Mikael, Willrodt Sina, Zimmerman Patrick, Stormbom Michael, Halme Erika, Agbese Mamia, Jantunen Marianne, Vakkuri Ville, Abrahamsson Pekka, Irfan Khan Irfan, Wali Syed, Lehto Martti, Pöyhönen Jouni, Simola Jussi, Porramo Pasi, Jokela Markus, Rantasalo Tuomas, Kentala Ville, Tammio Sami, Kaartinen Aku, Toivoniemi Timo, Alamaunu Jyrki, Biström Benjamin, Nummelin Miikka, Saari Timo, Salokannel Mikko, Hellberg Samuli, Isotahdon Joel, Eriksson Peter
(D4 Julkaistu kehittämis- tai tutkimusraportti tai -selvitys ) - Sea Ice Concentration Estimation Via Fusion of Sentinel-1 and AMSR2 Based on Encoder-Decoder Architecture (2023)
- Proceedings of the IEEE international conference on intelligent transportation systems
(A4 Vertaisarvioitu artikkeli konferenssijulkaisussa)
