Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.10177 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914156403228672 |
|---|---|
| author | Chhabra, Tishya Bajpai, Manisha Zesk, Walter Tibbits, Skylar |
| author_facet | Chhabra, Tishya Bajpai, Manisha Zesk, Walter Tibbits, Skylar |
| contents | We present an initial evaluation of NASA and IBM's Prithvi-EO-2.0 geospatial foundation model on shoreline delineation of small sandy islands using satellite images. We curated and labeled a dataset of 225 multispectral images of two Maldivian islands, which we publicly release, and fine-tuned both the 300M and 600M parameter versions of Prithvi on training subsets ranging from 5 to 181 images. Our experiments show that even with as few as 5 training images, the models achieve high performance (F1 of 0.94, IoU of 0.79). Our results demonstrate the strong transfer learning capability of Prithvi, underscoring the potential of such models to support coastal monitoring in data-poor regions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_10177 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Utilizing a Geospatial Foundation Model for Coastline Delineation in Small Sandy Islands Chhabra, Tishya Bajpai, Manisha Zesk, Walter Tibbits, Skylar Computer Vision and Pattern Recognition Artificial Intelligence We present an initial evaluation of NASA and IBM's Prithvi-EO-2.0 geospatial foundation model on shoreline delineation of small sandy islands using satellite images. We curated and labeled a dataset of 225 multispectral images of two Maldivian islands, which we publicly release, and fine-tuned both the 300M and 600M parameter versions of Prithvi on training subsets ranging from 5 to 181 images. Our experiments show that even with as few as 5 training images, the models achieve high performance (F1 of 0.94, IoU of 0.79). Our results demonstrate the strong transfer learning capability of Prithvi, underscoring the potential of such models to support coastal monitoring in data-poor regions. |
| title | Utilizing a Geospatial Foundation Model for Coastline Delineation in Small Sandy Islands |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2511.10177 |