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Segment anything, from space?

Publication ,  Conference
Ren, S; Luzi, F; Lahrichi, S; Kassaw, K; Collins, LM; Bradbury, K; Malof, JM
Published in: Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024
January 1, 2024

Recently, the first foundation model developed specifically for image segmentation tasks was developed, termed the "Segment Anything Model"(SAM). SAM can segment objects in input imagery based on cheap input prompts, such as one (or more) points, a bounding box, or a mask. The authors examined the zero-shot image segmentation accuracy of SAM on a large number of vision benchmark tasks and found that SAM usually achieved recognition accuracy similar to, or sometimes exceeding, vision models that had been trained on the target tasks. The impressive generalization of SAM for segmentation has major implications for vision researchers working on natural imagery. In this work, we examine whether SAM's performance extends to overhead imagery problems and help guide the community's response to its development. We examine SAM's performance on a set of diverse and widely studied benchmark tasks. We find that SAM does often generalize well to overhead imagery, although it fails in some cases due to the unique characteristics of overhead imagery and its common target objects. We report on these unique systematic failure cases for remote sensing imagery that may comprise useful future research for the community.

Duke Scholars

Published In

Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024

DOI

Publication Date

January 1, 2024

Start / End Page

8340 / 8350
 

Citation

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Ren, S., Luzi, F., Lahrichi, S., Kassaw, K., Collins, L. M., Bradbury, K., & Malof, J. M. (2024). Segment anything, from space? In Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024 (pp. 8340–8350). https://doi.org/10.1109/WACV57701.2024.00817
Ren, S., F. Luzi, S. Lahrichi, K. Kassaw, L. M. Collins, K. Bradbury, and J. M. Malof. “Segment anything, from space?” In Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024, 8340–50, 2024. https://doi.org/10.1109/WACV57701.2024.00817.
Ren S, Luzi F, Lahrichi S, Kassaw K, Collins LM, Bradbury K, et al. Segment anything, from space? In: Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024. 2024. p. 8340–50.
Ren, S., et al. “Segment anything, from space?Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024, 2024, pp. 8340–50. Scopus, doi:10.1109/WACV57701.2024.00817.
Ren S, Luzi F, Lahrichi S, Kassaw K, Collins LM, Bradbury K, Malof JM. Segment anything, from space? Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024. 2024. p. 8340–8350.

Published In

Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024

DOI

Publication Date

January 1, 2024

Start / End Page

8340 / 8350