Work on the advanced science-based AI algorithms. My focus is on reliably making the correct decisions outside of the training and testing data. Together with the general scientific principles, the algorithms, designed by me, use the concepts of emergence, self-organisation; artificial life simulations and explainable artificial intelligence. 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
- Object based context aware self-learning network for land cover classification (Dynland-2)
- 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., Automatic tree species classification from Sentinel 2 images using deficient inventory data, 2020 17th Biennial Baltic Electronics Conference (BEC), Tallinn, Estonia, pp. 1-6, 2020.