This group involves in several research projects with a budget of more than €1 million. The projects are in collaboration with different universities, research institutes, and industries.
Main Projects

Artificial Intelligence systems for enhancing sensing technologies and scientific discoveries  

Principal Investigator: Martha Arbayani Zaidan

Funder: Academy of Finland Research Fellowship

Budget: € 937,803 (€ 656,464 funded by the Academy of Finland + € 281,339 funded by the University of Helsinki)

Project duration: 01/09/2023 → 31/08/2027

This project will develop and implement Artificial Intelligence (AI) technology to improve the quality of data generated at environmental research stations. The project development and evaluation will be carried out mainly at the Station for Measuring Ecosystem-Atmosphere Relations (SMEAR stations) network nationally and internationally. The outcome of the project will lead to the automation of the SMEAR infrastructure, where scientists' involvement in performing repetitive tasks can be minimized and data quality improved. This will be achieved by AI automation of data quality control and instrument calibration; these time and error-prone tasks are currently performed manually by scientists. The AI implementation will be beneficial on a global scale as the number of research stations grows across the world.

Collaborators: CSC IT for Sciences (Finland), Finnish Meteorological Institute (FMI, Finland), Helsinki Region Environmental Services (HSY, Finland), Purdue University (US), University of Edinburgh (UK), Cyprus Institute (Cyprus), TU Delft (Netherlands), University of Eastern Finland (Kuopio, Finland) and Nanjing University (China). 

FunSNM: Fundamental principles of sensor network metrology

Principal Investigators: Tuukka Petäjä, Tareq Hussein, Martha Arbayani Zaidan

Funder: European Commission Joint Research Centre - European Partnership on Metrology

Budget: € 110,000 (total funding of € 2,589,828)

Project duration: 01/09/2023 → 31/08/2026

Project website: FunSNM

Sensor networks are used in a large number of fields but are struggling with data quality of varying degrees, with unknown measurement uncertainty and lack of traceability to the SI limiting their applicability. To overcome these issues, this project will address the metrological aspects of sensor networks, covering the uncertainty propagation, data quality metrics and SI-traceability in generic sensor networks, as well as the assessment, infrastructure, and risk analysis of distributed sensor networks alongside software frameworks and semantics via automated application of developed methods. The applicability of the methods, tools, and concepts will be demonstrated in typical real-world sensor networks.

Collaborators: VTT (Finland), CMI (Czech Republic), DTI (Denmark), FORCE Technology (Denmark), IPQ (Portugal), LNE (France), METROSERT (Estonia), PTB (Germany), VSL (Netherlands), Airparif (Paris, France), FZ-JUELICH (Germany), NPL (UK), University of Cambridge (UK), VINS (Serbia), CCPI (UK), Random Red (Croatia), Vaisala (Finland), METAS (Switzerland).

  • Project name: Knowledge and climate services from an African observation and Data research Infrastructure (KADI)
  • IRON contribution: is in performing AI-based data assimilation and data analysis on satellite remote sensing data and vast air quality sensor networks across big cities in Africa.

ATMDATA: Automatic aTMospheric Data Analysis Tools based on AI technologies

Principal Investigators: Martha Arbayani Zaidan, Tuomo Nieminen, Sasu Tarkoma

Funder: Helsinki Institute for Information Technology (HIIT)

Budget: ~ € 10,000 

Project duration: 01/09/2023 → 30/11/2023


Deep learning for the estimation of optical properties in hyperspectral imagery of forests

Principal Investigators: Martha Arbayani Zaidan, Jonathan Atherton, Jaana Back, Sasu Tarkoma

Funder: Helsinki Institute for Information Technology (HIIT) and Academy of Finland

Budget: ~ € 20,000 

Project duration: 01/09/2023 → 29/02/2024

Other Projects
  • Project name: Non-CO2 Forcers and Their Climate, Weather, Air Quality and Health Impacts (FOCI)
  • IRON contribution: is in AI-based tools for automatic New-Particle Formation identification and analysis.