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ZENODO
Dataset . 2026
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
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IceBoost v2.0 model - Glacier complex products for RGI v6.0 and RGI v7.0

Authors: Maffezzoli, Niccolò; Rignot, Eric; Barbante, Carlo; Morlighem, Mathieu; Petersen, Troels; Vascon, Sebastiano;

IceBoost v2.0 model - Glacier complex products for RGI v6.0 and RGI v7.0

Abstract

The repository contains the Glacier Complex products related to Randolph Glacier Inventory v.6 and v.7. The products are generated using the IceBoost v2.0 model. RGI v.6: regional folders from 1 to 19. RGI v.7: regional folders from 1 to 19. Each .tif file contains the distributed ice thickness of all glaciers merged together. The files have the following format: iceboost_20251009_rgixx_vyy_epsg_zzzz.tif - xx: value from 1 to 19- yy: 62 for RGI6 or 70G for RGI7.- zzzz: epsg regional code. (example iceboost_20251009_rgi4_v70G_epsg_32617.tif). File Data layers: IceBoost v2.0-modeled ice thickness Monte Carlo-modeled ice thickness uncertainty Jensen Gap Surface Elevation (TanDEM-X Edited DEM v1, more info here) Geoid Height (EIGEN-6C4 gravity field) Data type: float32.CRS: coordinates, measured in meters within a specific UTM zone.Spatial resolution: 100 meters.Nodata: np.nan File Attributes: Ice volume (with uncertainty): complex ice volume (with uncertainty). Ice volume below sea level (with uncertainty): complex ice volume below sea level (with uncertainty). Area: complex area. Ice thickness measurements contained within the complex, if any: latitude, longitude, ice thickness. Note: individual glaciers may be produced with a finer resolution than 100 meters. The complex product however is generated with a 100 resolution. For smaller glaciers we refer the users to the individual glacier product at https://zenodo.org/records/17724512. =============== Additional references: Randolph Glacier Inventory: https://www.glims.org/rgi_user_guide/welcome.html To download individual glacier files: see https://zenodo.org/records/17724512 To download the training datasets, the trained model: see https://zenodo.org/records/17724512 IceBoost Web Visualizer at: https://nmaffe.github.io/iceboost_webapp/ IceBoost code on Github at: https://github.com/nmaffe/iceboost ===============

Keywords

Machine Learning, Randolph Glacier Inventory, Glaciers

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average