The Spatiotemporal Data Analysis (SDA) research group does research on three focus areas: GNSS signal and sensor data protection, computer vision and sensor fusion, and sustainable smart cities by reinforcement learning algorithm development.
Sustainable smart cities by reinforcement learning algorithm development

In the fight against climate change and coping with the pressures arising from urbanisation, cities worldwide make solutions modifying pollutant emissions and thermal comfort. At present, road transport contributes a significant amount to the total carbon dioxide emissions in the EU. Thus, cities look for practical strategies to make their transport system more efficient and sustainable, and SDA Group is developing novel reinforcement learning methods to aid in this goal while maximising the air quality and minimising the carbon dioxide emissions.

Selected publications

  1. Joanne C. Demmler, Ákos Gosztonyi, Yaxing Du, Matti Leinonen, Laura Ruotsalainen, Leena Järvi, Sanna Ala-Mantila (2021): PLOS ONE,
    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
  2. Klavdiia Bochenina, Anton Taleiko, Laura Ruotsalainen: Simulation-based origin-destination matrix reduction: a case study of Helsinki city area, SUMO User Conference 2023.


  1. Sustainable urban development emerging from the merger of cutting-edge Climate, Social and Computer Sciences (CouSCOUS). This project is funded by the Research Council of Finland for 2020-2024.
  2. Artificial Intelligence for Urban Low-Emission Autonomous Traffic (AIforLEssAuto). This project has received funding from the European Union – NextGenerationEU instrument and is funded by the Research Council of Finland for 2022-2024.
GNSS signal and sensor data protection

Availability of a global navigation and timing solution provided by GNSS has accelerated the appearance of various applications supporting sustainable development in the forms of improved safety, decreased environmental burden, new business opportunities and increased quality of life. However, the applications have demands on the PVT reliability, availability and accuracy that GNSS was not developed to support. The need for an ubiquitous GNSS that can adapt to the signal environment is self-evident. The development of accurate deep learning (DL) methods has provided huge improvements in computation in many disciplines. However, GNSS and navigation field has been slow to introduce DL into research. SDA group provides novel machine learning methods for improving the performance of a GNSS receiver and providing ubiquitous adaptive GNSS.

Selected publications

  1. 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
  2. Arul Elango, Ahmed Al-Tahmeesschi, Mikko Saukkoriipi, Titti Malmivirta, Laura Ruotsalainen (2022): WHITE PAPER: Protecting GNSS Against Intentional Interference
  3. 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


  1. Resilience and security of geospatial data for critical infrastructures (Reason)
  2. Advanced RFI Detection, Alerting and Analysis System II (ARFIDAAS2)

Master's theses

  1. Detecting Anomalies in GNSS Signals with Complex-valued LSTM Networks (2022).


  1. Yle interviewed Professor Laura Ruotsalainen, the leader of SDA group, about GNSS signal jamming (9 Jan 2024, in Finnish). 
Computer vision and sensor fusion

Computer vision provides crucial information for various applications requiring reliable navigation and situational awareness. SDA group develops novel methods for Simultaneous Localization and Mapping (SLAM) in challenging environments and 3D object tracking for improved safety for autonomous systems.


  1. Niclas Joswig, Juuso Autiosalo, Laura Ruotsalainen (2023): Machine Vision and Applications, Improved deep depth estimation for environments with sparse visual cues
  2. Laura Ruotsalainen, Aiden Morrison, Maija Mäkelä, Jesperi Rantanen, Nadezda Sokolova (2021): IEEE Sensors Journal,
    Improving Computer Vision Based Perception for Collaborative Indoor Navigation


  1. Artifical Intelligence for Industrial Vision (AIV)
  2. Matine

Mas­ter's theses

  1. Monocular 3D Object Detection And Tracking in Industrial Settings (2021).
  2. Evaluation of Deep Learning-based SLAM in Industry Vision (2021)
  3. Non-Lambertian surfaces and their challenges in computer vision (2022).


  1. Konenäkö ohjaa jo nostureita: tutkimus tähtää tarkempaan navigointiin (15.1.2020, in Finnish)


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