Hanna Meyer
Biographic Data
| ID | 8003370 |
|---|---|
| NAME | Hanna Meyer |
| GIVEN NAMES | Hanna |
| FAMILY NAME | Meyer |
| SIGNATURE | MEYER H |
| AFFILIATIONS | University of Münster |
| ORCID | 0000-0003-0556-0210 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| 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…
Using Gedi as training data for an ongoing mapping of landscape-scale dynamics of the plant area index
Leaf or plant area index (LAI, PAI) information is frequently used to describe vegetation structure in environmental science. While field measurements are time-consuming and do not scale to landscapes, model-based air- or space-borne remote-sensing methods have been used for many years for area-wide monitoring. As of 2019, NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission delivers a point-based LAI product with 25 m footprints and per…
No prominent works on this page.
Using Gedi as training data for an ongoing mapping of landscape-scale dynamics of the plant area index
Leaf or plant area index (LAI, PAI) information is frequently used to describe vegetation structure in environmental science. While field measurements are time-consuming and do not scale to landscapes, model-based air- or space-borne remote-sensing methods have been used for many years for area-wide monitoring. As of 2019, NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission delivers a point-based LAI product with 25 m footprints and per…
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…
Benchmarking (1 work) · Cartography (1 work) · Comparability (1 work) · Computer Science (1 work) · Domain (mathematical analysis) (1 work) · Ecology (1 work) · Environmental Science (1 work) · Geographic Information Systems Studies (1 work) · Geography (1 work) · Key (lock) (1 work)