Evelyn Uuemaa
Biographic Data
| ID | 6157687 |
|---|---|
| NAME | Evelyn Uuemaa |
| GIVEN NAMES | Evelyn |
| FAMILY NAME | Uuemaa |
| SIGNATURE | UUEMAA E |
| AFFILIATIONS | University of Tartu |
| ORCID | 0000-0002-0782-6740 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2015 |
| 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…
Nitrogen and phosphorus discharge from small agricultural catchments predicted from land use and hydroclimate
Urbanisation-related Landscape Change in Space and Time along Spatial Gradients near Roads
The quantification of landscape patterns and the changes caused by suburbanisation processes is essential to the understanding of the causes and consequences of the human-induced spatial patterns. By using spatial gradients along main roads near cities, we attempted to quantify the influence of roads on the suburbanisation process and settlement structure in Estonia. The rapid suburbanisation process that has been influenced by the revision of pl…
No prominent works on this page.
Urbanisation-related Landscape Change in Space and Time along Spatial Gradients near Roads
The quantification of landscape patterns and the changes caused by suburbanisation processes is essential to the understanding of the causes and consequences of the human-induced spatial patterns. By using spatial gradients along main roads near cities, we attempted to quantify the influence of roads on the suburbanisation process and settlement structure in Estonia. The rapid suburbanisation process that has been influenced by the revision of pl…
Nitrogen and phosphorus discharge from small agricultural catchments predicted from land use and hydroclimate
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…
Ecology (2 works) · Geography (2 works) · Land use (2 works) · Land Use and Ecosystem Services (2 works) · Agricultural land (1 work) · Agriculture (1 work) · Benchmarking (1 work) · Business (1 work) · Capital city (1 work) · Christian ministry (1 work)