Open, Cloud-Optimized, Analysis-Ready Global GEDI Satellite LiDAR Dataset for Land Surface Applications

Ho Y; Heisig J; Milenković M; Parente L; Hengl T; Simoes R

Forschungsartikel in Sammelband (Konferenz) | Peer reviewed

Zusammenfassung

Current satellite LiDAR missions, such as GEDI and ICESat2, provide billions of points annually that are typically not  cloud-optimized and require additional quality filtering before any further analysis. In this study, we present OpenLandMap GEDI (OLM-GEDI), a new open, cloud-optimized,  and global GEDI point dataset, for which we establish a  spatio-temporal structure to facilitate efficient access. We  show random access to OLM-GEDI achieves 20 seconds and  a minute for areas around 50-thousand and 3-million km2,  respectively. The OLM-GEDI STAC catalog is further established, which can be readily loaded into a local or cloud  computing environment, such as openEO. This open GEDI  dataset can be beneficial to future studies to enhance their  reproducibility and mitigate the complexity of handling large  GEDI data volumes (∼ 120 TiB) and quality filters.

Details zur Publikation

Herausgeber*innenP. Kempeneers, S. Lumnitz, S. Albani
BuchtitelProceedings of the 2025 Conference on Big Data from Space (BiDS'25)
Seitenbereich69-72
VerlagPublications Office of the European Union
ErscheinungsortRiga, Latvia
StatusVeröffentlicht
Veröffentlichungsjahr2025
KonferenzESA Big Data from Space (BiDS), Rig, Lettland
ISBN978-92-68-31935-2
DOI10.2760/2119408
StichwörterGlobal; satellite; LiDAR; GEDI; canopy; terrain; cloud-native format; Geoparquet; STAC; openEO

Autor*innen der Universität Münster

Heisig, Johannes
Professur für Geoinformatik (Prof. Pebesma)