Future smart cities must be based on sustainability principles; they must provide improved quality of life for all citizens, be safe and as emsission-free as possible. This goal requires radical reforms in the traffic, namely creation of automated ground vehicle ecosystems, and moving parts of the transportation of goods, probably also human, to the airspace by using Unmanned Aerial Vehicles (UAVs). Pedestrians and bicyclists must be included in traffic monitoring and controlling actions, a fact that has been largely neglected so far in the discussion about automated traffic. All these goals demand developmet of sophisticated spatiotemporal data analysis algorithms. In order to implement a functional traffic ecosystem assuring safe cooperation of all these actors, knowledge of their position, ability to predict their movements and capability to fuse all this information together is crucial. The Spatiotemporal Data Analysis research group  does research on estimation and machine learning algorithms to solve the remaining challenges for achieving these goals.

The biggest challenges in creating a smart city traffic ecosystem including autonomous vehicles are: providing accurate and reliable seamless navigation in all environments, situations and by using low-cost equipment and 2) creating situational awareness of the positions and anticipated motion of all actors in the ecosystem. The specific technologies that still require significant research effort are:

  • Design of estimation algorithms for fusing measurements from multiple sensors and systems for providing navigation information for indoor and urban environments and for mitigating the effects of intentional interference towards satellite positioning.
  • Design of machine learning algorithms for sensing the environment, motion of the actor being positioned and providing predictions of the motion of the actors based on their behaviour for creating an adaptive navigation system.
  • Design of algorithms for cooperative navigation to avoid collisions, control the traffic, e.g. traffic lights or vehicle speeds and to share information for improving the position solution of a user in unfavourable situation.
  • Computer Vision will be a core technology for navigation of autonomous vehicles and UAVs, but will also provide additional information for the traffic ecosystem. More sophisticated computer vision methods are needed for overcoming the challenges related to environmental changes (light, rain, snow, ice) and arising from the use of inexpensive equipment.
  • 5G signals will create great new opportunities for addressing all above developments. They will provide opportunities for more accurate positioning, ranging between different actors and thereby improved cooperative positioning, and for transmitting information fast between different users in the traffic ecosystem.

Ongoing projects

AIV - Artificial Intelligence for Industrial Vision

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



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 are the compilation and evaluation of a concise and robust indoor / outdoor pedestrian navigation system that includes a Test-
Bed collecting PNT related data and algortihms for computing an accurate and reliable navigation solution. (Funder: European Space Agency, 2019-2020)

5GIVE - 5G- assisted Ground-based Galileo-GPS receiver Group with Inertial and Visual Enhancement

The project aims to create 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)  will address 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 will be also addressed for both methods. (Funder: European Space Agency, 2019-2020)

CANDO - Collaborative Augmented Navigation for Defence Objectives

This project seeks to address 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 builds 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 - Research Alliance for Autonomous systems

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).

Towards sustainable smart citiesCollaborative navigationDrones in urban environmentSustainable transportation