Capacity building of using machine learning for processing geospatial data in geoecological investigations
ML4GEO project (2020-2023) (Project ID: 20TTCG-1E009) “Capacity building of using machine learning for processing geospatial data in geoecological investigations”, funded by the State Committee of Sciences of the Ministry of Education, Science, Culture and Sport of Armenia, in frames of the call for proposals on “Supporting capacity building of the research laboratories or groups”
Project coordinator - Shushanik Asmaryan
International cooperation - Prof. Fabio Dell’Acqua, Department of Electrical, Computer and Biomedical Engineering, University of Pavia
Funded by - Call: Supporting capacity building of the research laboratories or groups-2020, The Committee of Higher Education and Science of the Ministry of Education, Science, Culture and Sport of Armenia
The ML4GEO project is aimed at dеveloping the capacity of the CENS in using the innovative technological approaches toward machine learning for processing geospatial data on earth observations (EO) in geoecological investigations. The objectives of the project are as follows.
- To develop a comprehensive strategic development agenda of CENS on improving the (EO) data processing technologies.
- To implement a pilot research on developing ML models of RS data geoprocessing with the purpose of assessing spatial-temporal changes in atmospheric air temperatures in urban spaces in conditions of climate change.
- To create a favourable work environment for the GIS and RS Department’s young staff and to further improves their research and networking skills.
The expected results are.
- A novel five-year research strategy development agenda of the application of innovative technological approaches namely machine learning in environmental studies.
- To derive ML algorithms of satellite imagery - based modeling of air temperatures in urban space, the inclusion of which in the Armenian Data Cube ADC as a novel toolset for RS data processing.
- To set a multidisciplinary team of young specialists competent in using ML technologies and having good research and networking skills.