DroneKnowledge – Towards knowledge based export of small UAS remote sensing technology (DroneKnowledge)
Main funder
Funder's project number: 1711/31/2016
Funds granted by main funder (€)
- 363 000,00
Funding program
Project timetable
Project start date: 25/01/2017
Project end date: 31/12/2018
Summary
DroneKnowledge is a joint proposal of top reach institutes and companies in Finland in the field of drone remote sensing. Project’s fundamental objective is to investigate next generation of drone remote sensing technologies in order to help Finnish companies to make a breakthrough in global markets. It combines the Challenge Finland Phase 1 projects DroneKnowledge and AGRI_EAGLE. Collaborators cover most important phases of the value chain of drone remote sensing. Members of the research consortium are the DroneFinland-group at the Finnish Geospatial Research Institute in National Land Survey of Finland (FGI) (www.dronefinland.fi;@dronefinland), University of Jyväskylä, Department of Mathematical Information Technology (JYU), Natural Resources Institute Finland, Green Technology Unit (LUKE) and VTT Technical Center. Duration of the research project is 1.1.2017-31.12.2018. Total cost of the research project is 3 M€ and the applied Tekes contribution is 1.8 M€. Nine leading Finnish drone-related companies, several of which are either already in the global markets or aiming at entering the international markets have agreed to submit their company proposals; value of the company proposals is approximately 6 M€. Furthermore, seven important companies from the field will support the project with valuable inkind contributions. This indicates strong confidence of the Finnish industry to the commercial potential to the offered solutions as well to the consortium.
Principal Investigator
Primary responsible unit
Related publications and other outputs
- Chlorophyll Concentration Retrieval by Training Convolutional Neural Network for Stochastic Model of Leaf Optical Properties (SLOP) Inversion (2020) Annala, Leevi; et al.; A1; OA
- Multilabel segmentation of cancer cell culture on vascular structures with deep neural networks (2020) Rahkonen, Samuli; et al.; A1; OA
- Tree Species Identification Using 3D Spectral Data and 3D Convolutional Neural Network (2019) Pölönen, Ilkka; et al.; B3; OA