EGU24-7637, updated on 08 Mar 2024
https://doi.org/10.5194/egusphere-egu24-7637
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Svalbox – from an educational tool to systematic digitization of Svalbard

Kim Senger1, Peter Betlem1, Rafael Horota1, Tereza Mosočiová1,2, Nil Rodes1, and Aleksandra Smyrak-Sikora3
Kim Senger et al.
  • 1University Centre in Svalbard, Department of Arctic Geology, Longyearbyen, Norway (kims@unis.no, peterbe@unis.no, rafaelh@unis.no)
  • 2Tectonostratigraphic Research Group, Department of Geoscience, University of Oslo, Oslo, Norway
  • 3Department of Geoscience and Petroleum, Norwegian University of Science and Technology, Trondheim, Norway (aleksandra.a.smyrak-sikora@ntnu.no)

Field teaching and research in the High Arctic is costly, both economically and in terms of environmental impact. In the Norwegian archipelago of Svalbard (74-81°N, 15-35°E), the field season is defined by the extreme annual daylight cycle, with a 4 month long polar night and midnight sun from late April to late August. The University Centre in Svalbard (UNIS) in Longyearbyen serves as Norway’s field university. Arctic Geology courses at UNIS make use of the excellent vegetation-free outcrops exhibiting various lithologies and tectono-magmatic structures.

At UNIS, geological fieldwork is mostly conducted in the snow free summer with focus on coastal localities accessible by hiking and boats. The snow-covered “spring” season in March and April allows access to inland locations on snowmobiles, but only steep cliffs are snow-free and accessible for investigations.  Irrespective of the time of the year, the harsh Arctic weather conditions, presence of wildlife (i.e. polar bears) and safe access to outcrops (e.g., strenuous hiking in steep terrain) routinely requires adjusting the field plans. In addition, the short field season(s) often only allow relatively short single field site visits. To make most efficient use of the field excursions, we have since 2016 built and developed Svalbox – an effort to systematically digitize Svalbard’s outcrops through digital outcrop models (DOMs) and photospheres integrated with other geoscientific data sets (maps, geophysical data, terrain models, borehole data etc.). 

Starting as an educational tool at UNIS to facilitate year-round (digital) fieldwork and quantitative analyses on outcrops, Svalbox has in recent years become an important resource for the wider geoscientific community. Svalbox comprises three main elements; 1) A database of freely accessible DOMs. 2) An open portal for visualising drone-based virtual field trips (VFTs) and 3) Thematic data packages integrating various data sets for UNIS courses or specific research projects. 

In this contribution, we present all these three Svalbox elements. The database itself, visualised via www.svalbox.no/map, offers a growing number of DOMs and photospheres (i.e. 360 images) from all over Svalbard. Both incorporate georeferenced photographs acquired with consumer cameras and drones (DJI Mavic 2 and 3 in particular). The DOMs are processed with structure-from-motion photogrammetry using the Metashape software. DOMs and photospheres are fully downloadable, including the processing reports  and all associated input data , facilitating reprocessing if needed. Curated data packages are integrated as VFTs and are accessible through a dedicated web portal, www.vrsvalbard.com/map with thematic groupings for specific UNIS courses and larger-scale research projects. 

Thematic data packages are generated from UNIS-internal databases (onshore and offshore seismic data, boreholes, digitized maps, cross sections, and sedimentary logs in publications etc.) and openly available datasets (digital terrain models, bathymetry, geophysical grids, maps etc.)  where data are spatially connected in single software projects (e.g., Petrel, GPlates) for specific courses or projects. 

At this stage, we strive for expanded usage of Svalbox beyond UNIS. In particular, we invite the geoscience and data analytics community to use the exponentially growing number of DOMs to test and train machine learning algorithms for (semi-)automatic interpretation of DOMs.

How to cite: Senger, K., Betlem, P., Horota, R., Mosočiová, T., Rodes, N., and Smyrak-Sikora, A.: Svalbox – from an educational tool to systematic digitization of Svalbard, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7637, https://doi.org/10.5194/egusphere-egu24-7637, 2024.