99b17382-a7ef-41ab-96fc-c1818c83def1 English Dataset German Aerospace Center (DLR) geoservice@dlr.de 2024-12-09T10:52:38 ISO 19115-1:2014/19139 2003/Cor.1:2006 EPSG:3035 Alpine Glacier Crevasses - Stubai, Oetztal, Grossglockner, Ortler, Piz Palue, 0.2m 2024-12-01T00:00:00 https://geoservice.dlr.de/catalogue/srv/metadata/99b17382-a7ef-41ab-96fc-c1818c83def1 DOI: 10.15489/fnjn62mbz324 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. 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. German Aerospace Center (DLR) celia.baumhoer@dlr.de German Aerospace Center (DLR) geoservice@dlr.de https://geoservice.dlr.de/catalogue/srv/api/records/99b17382-a7ef-41ab-96fc-c1818c83def1/attachments/crevasse_ql.png large_thumbnail https://geoservice.dlr.de/catalogue/srv/api/records/99b17382-a7ef-41ab-96fc-c1818c83def1/attachments/crevasse_ql_s.png thumbnail DLR EOC Alps glacier crevasse mountaineering glacier modelling high-resolution deep neural network opendata Geology GEMET - INSPIRE themes, version 1.0 2008-06-01 geonetwork.thesaurus.external.theme.httpinspireeceuropaeutheme-theme Nutzungseinschränkungen: Das DLR ist nicht haftbar für Schäden, die sich aus der Nutzung ergeben. / Use Limitations: DLR not liable for damage resulting from use. Es gelten keine Zugriffsbeschränkungen Nutzungsbedingungen: Lizenz, https://creativecommons.org/licenses/by/4.0 / Terms of use: License, https://creativecommons.org/licenses/by/4.0 {"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)"} 400 English geoscientificInformation 9.41 12.65 46.36 47.35 2019-08-01T00:00:00Z 2022-12-01T00:00:00Z GeoTIFF https://download.geoservice.dlr.de/GLACIER_CREVASSES/files/Stubai_Oetztal_Grossglocker_PizPalue/ WWW:LINK-1.0-http--link HTTP Download Service https://geoservice.dlr.de/web/datasets/glacier_crevasses EOC Geoservice Dataset EOC Geoservice Dataset Conformity_001 INSPIRE Commission Regulation (EU) No 1089/2010 of 23 November 2010 implementing Directive 2007/2/EC of the European Parliament and of the Council as regards interoperability of spatial data sets and services 2010-12-08 See the referenced specification. true Apline glacier crevasse boundaries detected with a multitask neural network (HED-Unet) from high-resolution (0.2 m) aerial imagery. 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).