- Titel
- Automatic boulder detection applied to the Stone Garden (Altai Mountains, Russia)
- Untertitel
- techniques and outcomes
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-985763
- Konferenz
- 5th Virtual Geoscience Conference. Dresden, 21. - 22. September 2023
- Quellenangabe
- VGC 2023 - Unveiling the dynamic Earth with digital methods
Herausgeber: Helmholtz-Institut Freiberg für Ressourcentechnologie
Herausgeber: Technische Universität Dresden
Erscheinungsort: Dresden
Erscheinungsjahr: 2023
Seiten: 50-51 - Erstveröffentlichung
- 2023
- DOI
- https://doi.org/10.25368/2025.178
- Abstract (EN)
- The demand for surface and sub-surface detection of boulder fields is growing. While being most developed for planetary sciences (e.g. HOOD et. al., 2022), in Earth studies it is mainly useful for offshore spatial planning (e.g., wind farms, platforms, pipelines) and ecosystem research. However, most offshore areas lack high-reso-lution non-confidential bathymetric and acoustic imagery data to apply and evaluate boulder detection techniques. In our project, we examine a large boulder field in the Altai Mountains (“Stone Garden”), which pro-vides an ideal location to test different techniques and design robust detection workflows. These outcomes can be further applied to offshore studies.
- Freie Schlagwörter (EN)
- boulder detection, deep learning, GIS, Altai Mountains
- Klassifikation (DDC)
- 550
- Klassifikation (RVK)
- RB 10081
- Herausgeber (Institution)
- Helmholtz-Institut Freiberg für Ressourcentechnologie, Freiberg
- Technische Universität Dresden, Dresden
- Version / Begutachtungsstatus
- publizierte Version / Verlagsversion
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-985763
- Veröffentlichungsdatum Qucosa
- 18.08.2025
- Dokumenttyp
- Konferenzbeitrag
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
CC BY 4.0