DeepBedMap: Using a deep neural network to better resolve the bed topography of Antarctica Article Swipe
To better resolve the bed elevation of Antarctica, we present DeepBedMap – a novel machine learning method that produces realistic Antarctic bed topography from multiple remote sensing data inputs. Our super-resolution deep convolutional neural network model is trained on scattered regions in Antarctica where high resolution (250 m) groundtruth bed elevation grids are available. The model is then used to generate high resolution bed topography in less well surveyed areas. DeepBedMap improves on previous interpolation methods by not restricting itself to a low spatial resolution (1000 m) BEDMAP2 raster image as its prior. It takes in additional high spatial resolution datasets, such as ice surface elevation, velocity and snow accumulation to better inform the bed topography even in the absence of ice-thickness data from direct ice-penetrating radar surveys. Our DeepBedMap model is based on an adapted Enhanced Super Resolution Generative Adversarial Network architecture, chosen to minimize per-pixel elevation errors while producing realistic topography. The final product is a four times upsampled (250 m) bed elevation model of Antarctica that can be used by glaciologists interested in the subglacial terrain, and by ice sheet modellers wanting to run catchment or continent-scale ice sheet model simulations. We show that DeepBedMap offers a more realistic topographic roughness profile compared to a standard bicubic interpolated BEDMAP2 and BedMachine Antarctica, and envision it to be used where a high resolution bed elevation model is required.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5194/tc-2020-74
- https://doi.org/10.5194/tc-2020-74
- OA Status
- gold
- Cited By
- 4
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3016741215
All OpenAlex metadata
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https://openalex.org/W3016741215Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5194/tc-2020-74Digital Object Identifier
- Title
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DeepBedMap: Using a deep neural network to better resolve the bed topography of AntarcticaWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-04-16Full publication date if available
- Authors
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Wei Ji Leong, Huw HorganList of authors in order
- Landing page
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https://doi.org/10.5194/tc-2020-74Publisher landing page
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https://doi.org/10.5194/tc-2020-74Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://doi.org/10.5194/tc-2020-74Direct OA link when available
- Concepts
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Elevation (ballistics), Geology, Digital elevation model, Interpolation (computer graphics), Snow, Terrain, Remote sensing, Image resolution, Ice sheet, Pixel, Geodesy, Geomorphology, Artificial intelligence, Image (mathematics), Computer science, Cartography, Geometry, Geography, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
- Citations by year (recent)
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2022: 2, 2021: 1, 2020: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.additional | 97 |
| abstract_inverted_index.available. | 54 |
| abstract_inverted_index.elevation, | 106 |
| abstract_inverted_index.interested | 175 |
| abstract_inverted_index.resolution | 46, 63, 85, 100, 225 |
| abstract_inverted_index.subglacial | 178 |
| abstract_inverted_index.topography | 23, 65, 116 |
| abstract_inverted_index.Adversarial | 141 |
| abstract_inverted_index.Antarctica, | 8, 215 |
| abstract_inverted_index.groundtruth | 49 |
| abstract_inverted_index.restricting | 79 |
| abstract_inverted_index.topographic | 203 |
| abstract_inverted_index.topography. | 153 |
| abstract_inverted_index.accumulation | 110 |
| abstract_inverted_index.interpolated | 211 |
| abstract_inverted_index.simulations. | 194 |
| abstract_inverted_index.architecture, | 143 |
| abstract_inverted_index.convolutional | 33 |
| abstract_inverted_index.glaciologists | 174 |
| abstract_inverted_index.ice-thickness | 122 |
| abstract_inverted_index.interpolation | 75 |
| abstract_inverted_index.continent-scale | 190 |
| abstract_inverted_index.ice-penetrating | 126 |
| abstract_inverted_index.super-resolution | 31 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/14 |
| sustainable_development_goals[0].score | 0.5400000214576721 |
| sustainable_development_goals[0].display_name | Life below water |
| citation_normalized_percentile.value | 0.63403622 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |