2025-01-12-wallace25a.md

title	openreview	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Exploring Segment Anything Foundation Models for Out of Domain Crevasse Drone Image Segmentation	CGkyjTXomz	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.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	wallace25a	0	Exploring Segment Anything Foundation Models for Out of Domain Crevasse Drone Image Segmentation	255	268	255-268	255	false	Wallace, Steven and Durrant, Aiden and Harcourt, William David and Hann, Richard and Leontidis, Georgios	given family Steven Wallace given family Aiden Durrant given family William David Harcourt given family Richard Hann given family Georgios Leontidis	2025-01-12		Proceedings of the 6th Northern Lights Deep Learning Conference (NLDL)	265	inproceedings	date-parts 2025 1 12	https://raw.githubusercontent.com/mlresearch/v265/main/assets/wallace25a/wallace25a.pdf