Tools
Tools
Wallace, Steven, Durrant, Aiden 
ORCID: https://orcid.org/0000-0002-8375-4523, Harcourt, William David, Hann, Richard and Leontidis, Georgios
(2025)
Exploring Segment Anything Foundation Models for Out of Domain Crevasse Drone Image Segmentation.
Proceedings of Machine Learning Research, 265.
pp. 255-268.
ISSN 2640-3498
Preview |
PDF (25_Exploring_Segment_Anything_)
- Published Version
Available under License Creative Commons Attribution. Download (8MB) | Preview |
Abstract
In this paper, we explore the application of Segment Anything (SAM) foundation models for segmenting crevasses in Uncrewed Aerial Vehicle (UAV) images of glaciers. We evaluate the performance of the SAM and SAM 2 models on ten high-resolution UAV images from Svalbard, Norway. Each SAM model has been evaluated in inference mode without additional fine-tuning. Using both automated and manual prompting methods, we compare the segmentation quantitatively using Dice Score Coefficient (DSC) and Intersection over Union (IoU) metrics. Results show that the SAM 2 Hiera-L model outperforms other variants, achieving average DSC and IoU scores of 0.43 and 0.28 respectively with automated prompting. However, the overall off-the-shelf performance suggests that further improvements are still required to enable glaciologists to examine crevasse patterns and associated physical processes (e.g. iceberg calving), indicating the need for further fine-tuning to address domain shift challenges. Our results highlight the potential of segmentation foundation models for specialised remote sensing applications while also identifying limitations in applying them to high-resolution UAV images, as well as ways to enhance further model performance on out-of-domain glacier imagery, such as few-shot and weakly supervised learning techniques.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | artificial intelligence,software,control and systems engineering,statistics and probability ,/dk/atira/pure/subjectarea/asjc/1700/1702 |
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Data Science and AI |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 26 Aug 2026 11:17 |
| Last Modified: | 26 Aug 2026 11:17 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/104367 |
| DOI: | issn:2640-3498 |
Downloads
Downloads per month over past year
Actions (login required)
 |
View Item |