Marta Jemeljanova
Biographic Data
| ID | 8536323 |
|---|---|
| NAME | Marta Jemeljanova |
| GIVEN NAMES | Marta |
| FAMILY NAME | Jemeljanova |
| SIGNATURE | JEMELJANOVA M |
| AFFILIATIONS | University of Latvia |
| ORCID | 0000-0002-7063-2236 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Navigating challenges in spatial machine learning
Spatial machine learning has matured considerably in recent years, providing powerful approaches for mapping and prediction. However, many challenges remain in achieving methodological robustness, transparency, and comparability. This paper examines six key methodological themes – validation, assessment, uncertainty quantification, algorithm design, software implementation, and reporting – and highlights the need for greater rigor to ensure relia…
Bedrock surface topography of Latvia
A new map and digital bedrock surface elevation model of Latvia is presented with a horizontal resolution of 250 m. The local bedrock comprises largely undisturbed layers of Palaeozoic and Mesozoic sedimentary rocks covered by up to 200 m thick Quaternary strata composed mostly of glacigenic and marine sediments. The bedrock surface elevation model and the corresponding map were created by tessellation interpolation of the bedrock surface based o…
No prominent works on this page.
Bedrock surface topography of Latvia
A new map and digital bedrock surface elevation model of Latvia is presented with a horizontal resolution of 250 m. The local bedrock comprises largely undisturbed layers of Palaeozoic and Mesozoic sedimentary rocks covered by up to 200 m thick Quaternary strata composed mostly of glacigenic and marine sediments. The bedrock surface elevation model and the corresponding map were created by tessellation interpolation of the bedrock surface based o…
Navigating challenges in spatial machine learning
Spatial machine learning has matured considerably in recent years, providing powerful approaches for mapping and prediction. However, many challenges remain in achieving methodological robustness, transparency, and comparability. This paper examines six key methodological themes – validation, assessment, uncertainty quantification, algorithm design, software implementation, and reporting – and highlights the need for greater rigor to ensure relia…
Bedrock (1 work) · Benchmarking (1 work) · Borehole (1 work) · Comparability (1 work) · Digital elevation model (1 work) · Domain (mathematical analysis) (1 work) · Geographic Information Systems Studies (1 work) · Geological Formations and Processes Exploration (1 work) · Geology (1 work) · Geometry (1 work)