Geoinformatics

Global challenges such as the uneven distribution of natural resources, security and climate issues, urban development and sprawl and a growing demand for energy all involve a significant spatial component. Our mission is to conduct research and teaching in the collection, management and analysis of geospatial information for a wide range of themes and scales, from local to global.
Research areas
Our research areas include Geodesy, Geoinformation technology , Digital photogrammetry and laser scanning and Remote sensing. Learn more about our teaching activities!
Photogrammetry, laser scanning, and other 3D sensing technologies are the main methods for measuring and modelling our living environment in 3D, 4D and virtual reality from point clouds and images. Our team’s research focuses on developing modern close-range mapping solutions and applications as mobile mapping methods and the automation of data processing for 3D modelling. Our on-going projects include the Centre of Excellence in Laser Scanning Research (2014-2019) and the Academy of Finland’s Strategic Research Funding Pointcloud project (2015-2021).
Professor: Matti Vaaja
Geodesy is the age-old science of where things on or close to the Earth's surface are located. Precisely determining the location of objects, or of oneself while navigating, is challenging. Today's technology of choice is satellite positioning, using global navigation satellite systems (GNSS) deployed by several spacefaring nations. In support of this, geodesists create and maintain geocentric co-ordinate reference frames as well as height systems useful for surveying, mapping and navigation. Our research focuses on the changing Earth including her gravity field, sea level, height systems and movements of the Earth's crust.
Professor: Maaria Nordman
Geoinformation technology comprises techniques and theories adopted from computer science, information technology and applied mathematics. Spatial data, modeled either as discrete objects or continuous fields require special methods in storing, analysis and visualization because of its multidimensional nature and tendency to autocorrelation. Our research varies from algorithm development, spatial data analytics, modeling and geosimulation, spatial statistics, software development and spatial visualization to map design. Currently, we develop the Open Geospatial Information Infrastructure for Research (OGIIR) with funding from the Academy of Finland.
Professors: Kirsi Virrantaus, Henrikki Tenkanen
Remote sensing, or Earth observation, is the use data collected by satellite- or airborne sensors to detect phenomena on Earth using electromagnetic radiation. The amount of available remote sensing data is increasing exponentially. Our team’s remote sensing research focuses on monitoring forests from space using satellite images. We are leading experts in measuring and modeling the spectral and structural properties of forests, with a special focus on the complex structures of coniferous forests. Our on-going projects are funded by the European Research Council (ERC) and Academy of Finland.
Read more about our research: aalto.fi/remotesensing
Professor:Miina Rautiainen
Latest publications
Evaluating the Effectiveness of Land-Use Policies in Preventing the Risk of Coastal Flooding : Coastal Regions of Helsinki and Espoo
Hidden becomes clear : Optical remote sensing of vegetation reveals water table dynamics in northern peatlands
3D visualisations for communicative urban and landscape planning: What systematic mapping of academic literature can tell us of their potential?
OVI-3 : A NoSQL visual query system supporting efficient anti-joins
Self-assessment in student’s learning and developing teaching in geoinformatics–case of Geoportti self-assessment tool
Links between light availability and spectral properties of forest floor in European forests
Evaluation of a forest radiative transfer model using an extensive boreal forest inventory database
A multi-objective optimization strategy for timber forwarding in cut-to-length harvesting operations
Synergistic use of multi- and hyperspectral remote sensing data and airborne LiDAR to retrieve forest floor reflectance
Inversion of True Leaf Reflectance from Very High Spatial Resolution Hyperspectral Images
Research group members
Otakaari 4
Mechanical engineering 1, K1
