Potential of SIOS’s airborne imaging sensors in Svalbard
Bibliographic record
Abstract
Svalbard Integrated Arctic Earth Observing System (SIOS) is an international partnership of 26 scientific institutions from 9 countries studying the environment and climate in and around Svalbard. The key aims of SIOS are: (1) to develop an efficient observing system, (2) to share technology, experience, and data, (3) to close knowledge gaps, and (4) to decrease the environmental footprint of science. SIOS encourages the usage of airborne remote sensing platforms for research activities in Svalbard to complement in situ measurements and reduce the environmental footprint of research. SIOS member institution Norwegian Research Centre (NORCE) has installed and tested a suite of optical imaging sensors on the Lufttransport Dornier aircraft stationed in Longyearbyen as part of the SIOS-InfraNor project. Two optical sensors are installed onboard the Dornier aircraft (1) the PhaseOne IXU-180 RGB camera and (2) the HySpex VNIR-1800 hyperspectral sensor. The aircraft with these cameras is configured to acquire aerial RGB imagery and hyperspectral remote sensing data in addition to its regular transport operation in Svalbard. To date, SIOS has supported around 50 hours of flight time to acquire airborne data using Dornier aircraft in Svalbard for more than 20 scientific projects. Airborne imaging sensors include a variety of applications within glaciology, biology, hydrology, and other fields of Earth system science to understand the state of the environment of Svalbard. Mapping glacier crevasses, generating DEMs for glaciological applications, mapping and characterising earth (e.g., minerals, vegetation), ice (e.g., sea ice, icebergs, glaciers and snow cover) and ocean surface features (e.g. colour, chlorophyll) are examples of implementation. Aerial photos are also useful for monitoring the seasonal changes in snow, sea ice cover, and ocean colour. In 2021, SIOS conducted capacity building activities to train the next generation of polar scientists to use airborne imaging sensor data for their projects as part of the SIOS hyperspectral remote sensing training course (HSRS). This study presents a few selected applications from this course to demonstrate the potential of airborne imaging sensors in Svalbard. These include mapping water bodies (e.g. fjords, rivers), estimation of snow grain size, land cover classification, deriving chlorophyll, and mapping terrestrial vegetation. Preliminary results from these studies will be used to develop operational scientific applications and complement measurements from in-situ observations acquired by SIOS infrastructure in Svalbard. Eventually, these datasets will be valuable resources for calibration and validation activities for upcoming satellite hyperspectral missions, for example, the Copernicus Hyperspectral Imaging Mission for the Environment (CHIME).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".