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Hanna Meyer

Biographic Data

ID8003370
NAMEHanna Meyer
GIVEN NAMESHanna
FAMILY NAMEMeyer
SIGNATUREMEYER H
AFFILIATIONSUniversity of Münster
ORCID0000-0003-0556-0210
VERIFIEDYes
TOTAL WORKS2
TOTAL CITATIONS0
WORKS AS AUTHOR2
WORKS AS EDITOR0
FIRST PUBLICATION YEAR2023
LATEST PUBLICATION YEAR2026
H-INDEX0
  • Navigating challenges in spatial machine learning

    Open Access•Jakub Nowosad, Carmelo Bonannella et al.•ARTICLE•Erdkunde•2026

    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

    Open Access•Alice Ziegler, Johannes Heisig et al.•ARTICLE•Environmental Research Letters•2023

    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

    Open Access•Alice Ziegler, Johannes Heisig et al.•ARTICLE•Environmental Research Letters•2023

    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

    Open Access•Jakub Nowosad, Carmelo Bonannella et al.•ARTICLE•Erdkunde•2026

    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)

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