Alpine Glacier Crevasses - Stubai, Oetztal, Grossglockner, Ortler, Piz Palue, 0.2m

The crevasse dataset provides information on crevasse locations at 0.2 m spatial resolution for selected regions in the Alps. Information on crevasse locations is important for mountaineers and field researchers to plan a safe traverse over a glacier. This dataset is generated based on a multitask deep neural network for automated crevasse mapping from high-resolution airborne remote sensing imagery. The model was trained and evaluated over seven training and six test areas located in the Öetztal and Stubai Alps. By simultaneously preforming edge detection and segmentation tasks, the multitask model is able to robustly detect glacier crevasses of different shapes within different illumination conditions with a balanced accuracy of 86%. To prove large-scale applicability, this dataset includes high-resolution crevasse maps for the entire Öetztal and Stubai Alps based on imagery acquired within the years 2019 and 2020. Furthermore, high-quality crevasse maps for all glaciers surrounding Großglockner, Piz Palü, and Ortler were generated for available imagery in the years 2022 and 2023, repsectively. The here presented datasets can be integrated into hiking maps and digital cartography tools to provide mountaineers and field researcher with up-to-date crevasse information but also inform modelers on the distribution of stress within a glacier. Accompanying Publication: Baumhoer, C., Leibrock, S., Zapf, C., Beer, W., Kuenzer, C., in review. Automated crevasse mapping for Alpine glaciers: A multitask deep neural network approach. International Journal of Applied Earth Observation and Geoinformation.

Date (Creation): 2024-12-01T00:00:00

Citation identifier: https://geoservice.dlr.de/catalogue/srv/metadata/99b17382-a7ef-41ab-96fc-c1818c83def1

Presentation form: Digital map

Other citation details: DOI: 10.15489/fnjn62mbz324

Purpose: The glacier crevasse dataset provides detailed information on crevasse locations for the Stubai and Ötztal Alps, Großglockner, Piz Palü and Ortler at 0.2 m resolution. The dataset offers mountaineers and field researchers up-to-date crevasse information for safe route planning and informs modellers about the distribution of stress within a glacier.

Status: Completed

Author

German Aerospace Center (DLR) -

Point of contact

German Aerospace Center (DLR)

Maintenance and update frequency: As needed

Keywords

DLR
EOC
Alps
glacier
crevasse
mountaineering
glacier modelling
high-resolution
deep neural network
opendata

GEMET - INSPIRE themes, version 1.0

Geology

Use limitation: Nutzungseinschränkungen: Das DLR ist nicht haftbar für Schäden, die sich aus der Nutzung ergeben. / Use Limitations: DLR not liable for damage resulting from use.

Access constraints: Other restrictions

Other constraints: Es gelten keine Zugriffsbeschränkungen

Use constraints: Other restrictions

Other constraints: Nutzungsbedingungen: Lizenz, https://creativecommons.org/licenses/by/4.0 / Terms of use: License, https://creativecommons.org/licenses/by/4.0

Other constraints: {"id": "cc-by-4.0",

"name": "Creative Commons Namensnennung - 4.0 International (CC BY 4.0)",

"url": " http://dcat-ap.de/def/licenses/cc-by/4.0 ",

"quelle": "Copyright DLR (year of production)"}

Spatial representation type: Grid

Denominator: 400

Language: English

Character set: UTF8

Topic category

Geoscientific information

Begin date: 2019-08-01T00:00:00Z

End date: 2022-12-01T00:00:00Z

Unique resource identifier: EPSG:3035

Distribution format

GeoTIFF ()

OnLine resource: HTTP Download Service

OnLine resource: EOC Geoservice Dataset

Hierarchy level: Dataset

Domain consistency

Measure identification: INSPIRE / Conformity_001

Conformance result

Date (Publication): 2010-12-08

Explanation: See the referenced specification.

Pass: Yes

Statement: Apline glacier crevasse boundaries detected with a multitask neural network (HED-Unet) from high-resolution (0.2 m) aerial imagery.

Description: Processing: Crevasse locations are automatically detected by a deep neural network from official 0.2 m resolution orthophotos that were masked with most recent available glacier boundaries to focus on glaciated area only.

Quality Assurance: The prediction output of the deep neural network was validated against six test areas in the Stubai and Ötztal Alps. Crevasses are detected with a balanced accuracy of 86% and a F1-Score of 98% (79% macro-averaged).