The SDA group's research is divided into many projects with their respective goals and timelines. The complete list of on-going and past projects is below.
On-going projects

The consortium answers to the possible challenges on air quality by helping cities to plan climate healthy future urban areas, considering future traffic flows and population structures. CouSCOUS combines the fields of artificial intelligence, atmospheric and social sciences in a way that has not been done is this extent before and therefore advances the state of scientific research in all disciplines involved and provides decision makers and city planners globally with novel relevant information and tools for creating future sustainable cities. Consortia will utilize various detailed data sources, from grid-level population data to climate and traffic data and analyses them. The consortia will work tightly both together as well as in co-operation with relevant stakeholders. SDA group develops deep learning methods for traffic flow prediction and for providing recommendations for sustainable city planning. This project is funded by the Research Council of Finland for 2020-2024.

Selected publications

Joanne C. Demmler, Ákos Gosztonyi, Yaxing Du, Matti Leinonen, Laura Ruotsalainen, Leena Järvi, Sanna Ala-Mantila (2021)
A novel approach of creating sustainable urban planning solutions that optimise the local air quality and environmental equity in Helsinki, Finland: The CouSCOUS study protocol

AIforLEssAuto brings together atmospheric and computer scientists and traffic engineers in active dialogue with municipal stakeholders with the ultimate aim to understand how autonomous electrified traffic should be organized during the transition period in order to reduce CO2 emissions. This is achieved by

1) building a framework of computational modelling tools to evaluate the CO2 emissions originating from electrified automated vehicles, and

2) developing artificial intelligence based control from vehicle-level to city-center wide traffic-level in which CO2 emissions are minimized.

Such multidisciplinary approach has not been done to this extent before and thus AIforLEssAuto advances the state of scientific research in all disciplines involved and the novel combination will certainly lead to new, scientifically and societally, important breakthroughs. SDA develops reinforcement learning methods for traffic simulating city level future traffic. This project has received funding from the European Union – NextGenerationEU instrument and is funded by the Research Council of Finland for 2022-2024.

More details in the project page.

The project aims at developing cost-effective and reliable Visual Simultaneous Localization and Mapping (SLAM) and deep learning methods for 3D object tracking for improving industrial safety. Research is funded by a donation done by Konecranes for 2020-2021 and for 2023-2024.

Selected publications
Niclas Joswig, Juuso Autiosalo, Laura Ruotsalainen (2023)
Machine Vision and Applications
Improved deep depth estimation for environments with sparse visual cues

FCAI's video presenting AIV project
Win-win: Konecranes and FCAI collaborate to develop intelligent cranes

Published on 15.1.2020

Mas­ter's theses
Monocular 3D Object Detection And Tracking in Industrial Settings
Evaluation of Deep Learning-based SLAM in Industry Vision
Non-Lambertian surfaces and their challenges in computer vision

In MATINE, research supporting military national defence for the Defence Forces and the
Defence Administration is conducted. The project's aims are to promote the comprehensive security of society and the cooperation of authorities and advance the modern state-of-the-art research regarding military defence.

Past projects

The project created methods fusing GNSS, sensors and terrestrial positioning signals for seamless navigation of ground based users, especially automated vehicles. Research at the Department of Computer Science (UH) addressed the use of 5G-signals for improved positioning performance via two methods; by transmitting data for the high-accuracy hybrid PVT engine and by developing methods for ranging between vehicles in a platoon. Security and privacy requirement for the data was also addressed for both methods. (Funder: European Space Agency, 2019-2021)

Selected publications
Ashwin Rao, Martti Kirkko-Jaakkola, Laura Ruotsalainen (2021)
2021 Joint European Conference on Networks and Communications 6G Summit (EuCNC/6G Summit)
Dissemination of GNSS RTK using MQTT

Multiple complementary positioning sensors have to be used for managing varying positioning environments and for providing increased accuracy and integrity via sensor fusion. The main goals of the project were the compilation and evaluation of a concise and robust indoor / outdoor pedestrian navigation system that included a Test-Bed collecting PNT related data and algorithms for computing an accurate and reliable navigation solution. Funder: European Space Agency, 2019-2020.

This project addressed the personnel protection goal of the counter terrorism key priority through provision of enhanced situational awareness and blue force tracking within urban and indoor environments. To achieve this goal, the project builded on the concept of 'Collaborative Navigation' (CN) whereby members of a platoon operating in the same area are able to act as navigation references and communication nodes for one another. The CN concept provides two primary benefits:

  1. The enhancement of absolute and relative position information for every cooperating user, allowing extended indoor operation independent of satellite navigation, and
  2. The dramatic enhancement of situational awareness information to every member of the formation, even those that are beyond direct communications range due to the relay nature of the network.

Funder: NATO Science for Peace and Security for SINTEF, Norway and Finnish Geospatial Research Institute, Finland. University of Helsinki for research done in Finland, 2018-2020.

RAAS ( is the leading interdisciplinary innovation ecosystem and service platform for autonomous systems R&D in Finland. Spatiotemporal Data Analysis group is heavily involved in the Situational awareness, Autonomous Navigation & Intelligent Control Research Task Force (RTF) 2018-2020.

White paper: Safety Challenges of Autonomous Mobile Systems in Dynamic Unstructured Environments: Situational awareness, decision-making, autonomous navigation, & human-machine interface

University network for developing drone related research and education in Finland. Funder: Ministry of Culture and Education, 2021-2023.

The availability and reliability of user equipment localization is a key enabler for numerous applications. Some examples include autonomous driving and location based advertisement. While most of current solutions are based on Global Navigation Satellite Systems (GNSS), maintaining localization accuracy in densely built-up areas may not be achieved. One of the modern radio networks requirements (such as 5G and beyond) is to be able to track the mobile user equipment at all time. Thus, it may be used for complementing or replacing GNSS signals in case they are degraded or denied. This research aims to utilize received 5G signals combined with deep reinforcement learning to improve the user localization accuracy in 5G and beyond radio networks.

Selected publications
Ahmed Al-Tahmeesschi, Jukka Talvitie, Miguel López–Benítez, Laura Ruotsalainen (2022)
2022 International Conference on Localization and GNSS (ICL-GNSS)
Deep Learning-based Fingerprinting for Outdoor UE Positioning Utilising Spatially Correlated RSSs of 5G Networks

We constructed an automatized tool to predict the vulnerability of satellite navigation (GNSS) signals to ensure the continuity and resilience of location and timing services that are vital to a number of critical infrastructures, such as, search and rescue, electrical power grids, banking, security information systems, communications, and transport. We utilized the FinnRef reference network and the GNSS Finland monitoring service and enhanced it with temporary reference stations and signals of opportunity to monitor the GNSS signal quality in critical locations. Using the big data acquired with these networks, we computed future trends to forecast the critical failures in both positioning and timing information. The research contributed to the robustness of the Finnish timing service. We made this information available for the end users to enhance their capability for cyber security, situational awareness, and digital services.

SDA developed deep learning methods for detecting signal abnormalities and system failure and for mitigating their effects on critical infrastructure. Funder: Research Council of Finland 2020-2023.

Selected publications
Arul Elango, Sahar Ujan, Laura Ruotsalainen (2022)
2022 International Conference on Localization and GNSS (ICL-GNSS)
Disruptive GNSS Signal detection and classification at different Power levels Using Advanced Deep-Learning Approach

Arul Elango, Ahmed Al-Tahmeesschi, Mikko Saukkoriipi, Titti Malmivirta, Laura Ruotsalainen (2022)
WHITE PAPER: Protecting GNSS Against Intentional Interference

The CANDO II project brought together navigation experts from SINTEF (Norway) and the University of Helsinki to build upon the results and lessons learned within the CANDO project (2018-2019) to design and produce a cutting-edge navigation system to address the situational awareness (blue force tracking) and navigation needs of dismounted soldiers with support for human-machine teaming in conducting building clearing and urban operations. Full exchange of relevant research background and foreground by both institutions ensured the best possible technological outcome. The program goal of young researcher support was addressed through the involvement of graduate students from the University of Helsinki in training activities. Funder: NATO Science for Peace and Security 2021-2023.

Selected publications
Laura Ruotsalainen, Aiden Morrison, Maija Mäkelä, Jesperi Rantanen, Nadezda Sokolova (2021)
IEEE Sensors Journal
Improving Computer Vision Based Perception for Collaborative Indoor Navigation

Advanced detection, alerting and analysis system for intentional GNSS interference. Funder: European Space Agency, Navisp element 3 2021-2022.