Semantic segmentation of glaciological features across multiple remote sensing platforms with the Segment Anything Model (SAM)
Semantic segmentation is a critical part of observation-driven research in glaciology. Using remote sensing to quantify how features change (e.g. glacier termini, supraglacial lakes, icebergs, crevasses) is particularly important in polar regions, where glaciological features may be spatially small...
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ftdoajarticles:oai:doaj.org/article:c61a8d7cbbc2436e8c242bab2832f368 2024-01-07T09:44:26+01:00 Semantic segmentation of glaciological features across multiple remote sensing platforms with the Segment Anything Model (SAM) Siddharth Shankar Leigh A. Stearns C. J. van der Veen https://doi.org/10.1017/jog.2023.95 https://doaj.org/article/c61a8d7cbbc2436e8c242bab2832f368 EN eng Cambridge University Press https://www.cambridge.org/core/product/identifier/S0022143023000953/type/journal_article https://doaj.org/toc/0022-1430 https://doaj.org/toc/1727-5652 doi:10.1017/jog.2023.95 0022-1430 1727-5652 https://doaj.org/article/c61a8d7cbbc2436e8c242bab2832f368 Journal of Glaciology, Pp 1-10 Crevasses glacier mapping iceberg calving remote sensing sea ice Environmental sciences GE1-350 Meteorology. Climatology QC851-999 article ftdoajarticles https://doi.org/10.1017/jog.2023.95 2023-12-10T01:41:34Z Semantic segmentation is a critical part of observation-driven research in glaciology. Using remote sensing to quantify how features change (e.g. glacier termini, supraglacial lakes, icebergs, crevasses) is particularly important in polar regions, where glaciological features may be spatially small but reflect important shifts in boundary conditions. In this study, we assess the utility of the Segment Anything Model (SAM), released by Meta AI Research, for cryosphere research. SAM is a foundational AI model that generates segmentation masks without additional training data. This is highly beneficial in polar science because pre-existing training data rarely exist. Widely-used conventional deep learning models such as UNet require tens of thousands of training labels to perform effectively. We show that the Segment Anything Model performs well for different features (icebergs, glacier termini, supra-glacial lakes, crevasses), in different environmental settings (open water, mélange, and sea ice), with different sensors (Sentinel-1, Sentinel-2, Planet, timelapse photographs) and different spatial resolutions. Due to the performance, versatility, and cross-platform adaptability of SAM, we conclude that it is a powerful and robust model for cryosphere research. Article in Journal/Newspaper Journal of Glaciology Sea ice Directory of Open Access Journals: DOAJ Articles Journal of Glaciology 1 10 |
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Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Crevasses glacier mapping iceberg calving remote sensing sea ice Environmental sciences GE1-350 Meteorology. Climatology QC851-999 |
spellingShingle |
Crevasses glacier mapping iceberg calving remote sensing sea ice Environmental sciences GE1-350 Meteorology. Climatology QC851-999 Siddharth Shankar Leigh A. Stearns C. J. van der Veen Semantic segmentation of glaciological features across multiple remote sensing platforms with the Segment Anything Model (SAM) |
topic_facet |
Crevasses glacier mapping iceberg calving remote sensing sea ice Environmental sciences GE1-350 Meteorology. Climatology QC851-999 |
description |
Semantic segmentation is a critical part of observation-driven research in glaciology. Using remote sensing to quantify how features change (e.g. glacier termini, supraglacial lakes, icebergs, crevasses) is particularly important in polar regions, where glaciological features may be spatially small but reflect important shifts in boundary conditions. In this study, we assess the utility of the Segment Anything Model (SAM), released by Meta AI Research, for cryosphere research. SAM is a foundational AI model that generates segmentation masks without additional training data. This is highly beneficial in polar science because pre-existing training data rarely exist. Widely-used conventional deep learning models such as UNet require tens of thousands of training labels to perform effectively. We show that the Segment Anything Model performs well for different features (icebergs, glacier termini, supra-glacial lakes, crevasses), in different environmental settings (open water, mélange, and sea ice), with different sensors (Sentinel-1, Sentinel-2, Planet, timelapse photographs) and different spatial resolutions. Due to the performance, versatility, and cross-platform adaptability of SAM, we conclude that it is a powerful and robust model for cryosphere research. |
format |
Article in Journal/Newspaper |
author |
Siddharth Shankar Leigh A. Stearns C. J. van der Veen |
author_facet |
Siddharth Shankar Leigh A. Stearns C. J. van der Veen |
author_sort |
Siddharth Shankar |
title |
Semantic segmentation of glaciological features across multiple remote sensing platforms with the Segment Anything Model (SAM) |
title_short |
Semantic segmentation of glaciological features across multiple remote sensing platforms with the Segment Anything Model (SAM) |
title_full |
Semantic segmentation of glaciological features across multiple remote sensing platforms with the Segment Anything Model (SAM) |
title_fullStr |
Semantic segmentation of glaciological features across multiple remote sensing platforms with the Segment Anything Model (SAM) |
title_full_unstemmed |
Semantic segmentation of glaciological features across multiple remote sensing platforms with the Segment Anything Model (SAM) |
title_sort |
semantic segmentation of glaciological features across multiple remote sensing platforms with the segment anything model (sam) |
publisher |
Cambridge University Press |
url |
https://doi.org/10.1017/jog.2023.95 https://doaj.org/article/c61a8d7cbbc2436e8c242bab2832f368 |
genre |
Journal of Glaciology Sea ice |
genre_facet |
Journal of Glaciology Sea ice |
op_source |
Journal of Glaciology, Pp 1-10 |
op_relation |
https://www.cambridge.org/core/product/identifier/S0022143023000953/type/journal_article https://doaj.org/toc/0022-1430 https://doaj.org/toc/1727-5652 doi:10.1017/jog.2023.95 0022-1430 1727-5652 https://doaj.org/article/c61a8d7cbbc2436e8c242bab2832f368 |
op_doi |
https://doi.org/10.1017/jog.2023.95 |
container_title |
Journal of Glaciology |
container_start_page |
1 |
op_container_end_page |
10 |
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1787425824779010048 |