Environmental research with active sensors (ActiveSensing)
The research group of Environmental research with active sensors.
The research group of Environmental research with active sensors.
FinSig will use the Finnish National GNSS network ‘FinnRef’ in first-hand monitoring of the status of GNSS signal quality over Finland. A service architecture design will be developed and demonstrated, including a list of hardware, software, inter-modular communication, and system integration needs.
The project will carry out an investigation commissioned by the National Emergency Supply Agency (NESA), the final result of study will be a description of the provision and operation of GNSS services. The study surveys the supply of GNSS services and operators both in Finland and abroad and provides a description of the overall situation.
GNSS-Finland Service project will offer signal quality information in multiple frequencies for all four Global constellations, i.e., GPS, Galileo, GLONASS and BeiDou. In addition, it will offer alert messages to Traficom in case of detection of any predefined signal anomalies. Anomalies could be among other things interference on GNSS signal. This project is the successor of the project GNSS-Finland (GNSS Signal Quality Monitoring in Finland - FinSig) that ended in June 2019.
Making Finland a global leader of sustainable mineral industry requires continuous improvement of expertise in mining indstry. Mineral extraction industry demands for fast, cost efficient and safe remote sensing methods. The Kaivos Project works on efficient and safe identification of minerals.
The laser spectroscopy project utilizes a supercontinuum laser source.
The hyperspectral lidar (HSL) technology developed at the FGI produces non-contact spectral information in 3D.
The aim of this project is the development of active hyperspectral LiDAR prototype.
This project brings up novel spectroscopic and radiometric laser scanning methods to be applied in the change detection of the Boreal environment.
The RAGE project will develop automatic and reliable mineral detection based on AI machine learning based on active hyperspectral 3D sensing, indoor positioning, and real-time AI.
A standard lidar survey produces a point cloud consisting of the topographic information (e.g., position in x,y,z) and the intensity (I).