J.Sinica-Sinavskis is in computer science, a field at the remote sensing application between hyperspectral imaging, multispectral image processing, synthetic-aperture radar image processing, statistical modelling, and object based image analysis. J.Sinica- Sinavskis is working in the EDI in the area of image processing since 2006. J.Sinica- Sinavskis received the master degree in Mathematics from University of Latvia in 2009, and since 2019 he holds a Ph.D. in Computer Science at University of Latvia. He is a member of Latvian Association of Statisticians (LSA), The Remote Sensing and Photogrammetry Society (RSPSoc), and IEEE Geoscience and Remote Sensing Society (GRSS). He works closely with application experts, in order to focus the algorithm to solve the problem effectively.
- Object based context aware self-learning network for land cover classification (Dynland-2)
- Satellite remote sensing- based forest stock estimation technology (WoodStock) #ESIF
- Dynamic land use monitoring by fusion of satellite data (DynLand) #ESA
- Innovative technologies for acquisition and processing of biomedical images (InBiT) #ESIF
- Multi-model development technology for .NET application projects (MEDUS) #ESIF
- Cyber-physical systems, ontologies and biophotonics for safe&smart city and society (GUDPILS) #SRP (VPP)
- Remote sensing based system for forest risk factor monitoring (Forest Risk) #ESIF
- Sentinel for confidence in outdated maps: SentiMap #ESIF
- 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.