Work on the advanced science-based AI algorithms. Privately work on the innovative solution for the online collaborative space. The innovative work is related to P2P connections, user interface, long-term enterprise development, small team enterprise for large scale user base. Technology commercialization. Received education in the fields of statistics, mathematics, computer science, business.
Participation in projects
- Dynamic land use monitoring (NevKlas) #ESIF
- Dynamic land use monitoring by fusion of satellite data (DynLand) #ESA
- Satellite remote sensing- based forest stock estimation technology (WoodStock) #ESIF
- Self-learning technology for AML detection (AML) #ESIF
The most important publications
- R. Dinuls, G. Erins, A. Lorencs, I. Mednieks, and J. Sinica-Sinavskis, “Tree species identification in mixed Baltic forest using LiDAR and multispectral data,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 5, no. 2, pp. 594–603, 2012.
- Romans Dinuls, Ints Mednieks. (2018). Nonparametric Classification of Satellite Images.ICoMS 2018 Proceedings of the 2018 International Conference on Mathematics and Statistics Pages 64-68, Porto, Portugal — July 15 - 17, 2018
- R. Dinuls, A. Lorencs, I. Mednieks. "Using Consolidated Covariance Image for Discrimination of Habitats," Proceedings of the 13th Biennial Baltic Electronics Conference, Tallinn, Estonia, pp.299-302, 2012.
- R. Dinuls, A. Lorencs, I. Mednieks. “Performance Comparison of Methods for Tree Species Classification in Multispectral Images,” Electronics and Electrical Engineering. Kaunas: Technologija, 2011, No.5(111), pp. 119–122.
- Sinica-Sinavskis, J., Dinuls, R., Zarins, J., Mednieks, I. (2020). Automatic tree species classification from Sentinel 2 images using deficient inventory data, 17th Biennial Baltic Electronics Conference (BEC2020) Tallin, Estonia.