
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 ===============
Machine Learning, Randolph Glacier Inventory, Glaciers
Machine Learning, Randolph Glacier Inventory, Glaciers
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