{"dp_type": "Dataset", "free_text": "Machine Learning"}
[{"awards": "2136938 Tedesco, Marco", "bounds_geometry": ["POLYGON((-40 67.55,-39.611 67.55,-39.222 67.55,-38.833 67.55,-38.444 67.55,-38.055 67.55,-37.666 67.55,-37.277 67.55,-36.888 67.55,-36.499 67.55,-36.11 67.55,-36.11 67.28999999999999,-36.11 67.03,-36.11 66.77,-36.11 66.51,-36.11 66.25,-36.11 65.99,-36.11 65.73,-36.11 65.47,-36.11 65.21000000000001,-36.11 64.95,-36.499 64.95,-36.888 64.95,-37.277 64.95,-37.666 64.95,-38.055 64.95,-38.444 64.95,-38.833 64.95,-39.222 64.95,-39.611 64.95,-40 64.95,-40 65.21000000000001,-40 65.47,-40 65.73,-40 65.99,-40 66.25,-40 66.51,-40 66.77,-40 67.03,-40 67.28999999999999,-40 67.55))"], "date_created": "Mon, 07 Oct 2024 00:00:00 GMT", "description": "This dataset contains high-resolution satellite-derived snow/ice surface melt-related data on a common 100 m equal area grid (Albers equal area projection; EPSG 9822) over Helheim Glacier and surrounding areas in Greenland. The data is used as part of a machine learning framework that aims to fill data gaps in computed meltwater fraction on the 100 m grid using a range of methods, results of which will be published separately.\r\n\u003cbr/\u003e\u003cbr/\u003e\r\n\u003cbr/\u003e\u003cbr/\u003eThe data include fraction of a grid cell covered by meltwater derived from Sentinel-1 synthetic aperture radar (SAR) backscatter, satellite-derived passive microwave (PMW) brightness temperatures, snowpack liquid water content within the first meter of snow and atmospheric and radiative variables from the Mod\u00e9le Atmosph\u00e9rique R\u00e8gional (MAR) regional climate model, spectral reflectance in four wavelength bands from the Moderate Resolution Imaging Spectroradiometer (MODIS), a static digital elevation model (DEM), and an ice sheet mask. \r\n\u003cbr/\u003e\u003cbr/\u003eA similar dataset has also been produced for Larsen C ice shelf and is also available through the US Antarctic Program Data Center. \r\n\u003cbr/\u003e\u003cbr/\u003e\r\n\u003cbr/\u003e\u003cbr/\u003e\r\n\u003cbr/\u003e\u003cbr/\u003e", "east": -36.11, "geometry": ["POINT(-38.055 66.25)"], "keywords": "Antarctica; Climate Modeling; Cryosphere; Downscaling; Glaciers/ice Sheet; Glaciers/Ice Sheet; Glaciology; Greenland; Ice Sheet; Machine Learning; MAR; Remote Sensing; Sea Level Rise; Snow/ice; Snow/Ice; Surface Melt", "locations": "Greenland; Antarctica; Greenland", "north": 67.55, "nsf_funding_programs": "Polar Cyberinfrastructure", "persons": "Alexander, Patrick; Antwerpen, Raphael; Cervone, Guido; Fettweis, Xavier; L\u00fctjens, Bj\u00f6rn; Tedesco, Marco", "project_titles": "Collaborative Research: EAGER: Generation of high resolution surface melting maps over Antarctica using regional climate models, remote sensing and machine learning", "projects": [{"proj_uid": "p0010277", "repository": "USAP-DC", "title": "Collaborative Research: EAGER: Generation of high resolution surface melting maps over Antarctica using regional climate models, remote sensing and machine learning"}], "repo": "USAP-DC", "repositories": "USAP-DC", "science_programs": null, "south": 64.95, "title": "Surface melt-related multi-source remote-sensing and climate model data over Helheim Glacier, Greenland for segmentation and machine learning applications", "uid": "601841", "west": -40.0}, {"awards": "2136938 Tedesco, Marco", "bounds_geometry": ["POLYGON((-68.5 -65.25,-67.35 -65.25,-66.2 -65.25,-65.05 -65.25,-63.9 -65.25,-62.75 -65.25,-61.6 -65.25,-60.45 -65.25,-59.3 -65.25,-58.15 -65.25,-57 -65.25,-57 -65.652,-57 -66.054,-57 -66.456,-57 -66.858,-57 -67.25999999999999,-57 -67.66199999999999,-57 -68.064,-57 -68.466,-57 -68.868,-57 -69.27,-58.15 -69.27,-59.3 -69.27,-60.45 -69.27,-61.6 -69.27,-62.75 -69.27,-63.9 -69.27,-65.05 -69.27,-66.2 -69.27,-67.35 -69.27,-68.5 -69.27,-68.5 -68.868,-68.5 -68.466,-68.5 -68.064,-68.5 -67.66199999999999,-68.5 -67.25999999999999,-68.5 -66.858,-68.5 -66.456,-68.5 -66.054,-68.5 -65.652,-68.5 -65.25))"], "date_created": "Mon, 07 Oct 2024 00:00:00 GMT", "description": "This dataset contains high-resolution satellite-derived snow/ice surface melt-related data on a common 100 m equal area grid (Lambert azimuthal equal area projection; EPSG 9820) over Larsen C Ice Shelf and surrounding areas in Antarctica. The data is prepared to be used as part of a machine learning framework that aims to fill data gaps in computed meltwater fraction on the 100 m grid using a range of methods, results of which will be published separately.\r\n\u003cbr/\u003e\u003cbr/\u003e\u003cbr/\u003eThe data include fraction of a grid cell covered by meltwater derived from Sentinel-1 synthetic aperture radar (SAR) backscatter, satellite-derived passive microwave (PMW) brightness temperatures, snowpack liquid water content within the first meter of snow and atmospheric and radiative variables from the Mod\u00e9le Atmosph\u00e9rique R\u00e8gional (MAR) regional climate model, a static digital elevation model (DEM), and an ice sheet mask. \r\n\u003cbr/\u003e\u003cbr/\u003e\u003cbr/\u003eA similar dataset has been produced for Helheim Glacier, Greenland and is also available through the US Antarctic Program Data Center.", "east": -57.0, "geometry": ["POINT(-62.75 -67.25999999999999)"], "keywords": "Antarctica; Climate Modeling; Cryosphere; Downscaling; Glaciers/ice Sheet; Glaciers/Ice Sheet; Glaciology; Ice Shelf; Larsen C Ice Shelf; Machine Learning; MAR; Remote Sensing; Sea Level Rise; Snow/ice; Snow/Ice; Surface Melt", "locations": "Antarctica; Larsen C Ice Shelf", "north": -65.25, "nsf_funding_programs": "Polar Cyberinfrastructure", "persons": "Alexander, Patrick; Antwerpen, Raphael; Cervone, Guido; Fettweis, Xavier; L\u00fctjens, Bj\u00f6rn; Tedesco, Marco", "project_titles": "Collaborative Research: EAGER: Generation of high resolution surface melting maps over Antarctica using regional climate models, remote sensing and machine learning", "projects": [{"proj_uid": "p0010277", "repository": "USAP-DC", "title": "Collaborative Research: EAGER: Generation of high resolution surface melting maps over Antarctica using regional climate models, remote sensing and machine learning"}], "repo": "USAP-DC", "repositories": "USAP-DC", "science_programs": null, "south": -69.27, "title": "Surface melt-related multi-source remote-sensing and climate model data over Larsen C Ice Shelf, Antarctica for segmentation and machine learning applications", "uid": "601842", "west": -68.5}, {"awards": "1914698 Hansen, Samantha", "bounds_geometry": ["POLYGON((148 -71.5,150.4 -71.5,152.8 -71.5,155.2 -71.5,157.6 -71.5,160 -71.5,162.4 -71.5,164.8 -71.5,167.2 -71.5,169.6 -71.5,172 -71.5,172 -72.15,172 -72.8,172 -73.45,172 -74.1,172 -74.75,172 -75.4,172 -76.05,172 -76.7,172 -77.35,172 -78,169.6 -78,167.2 -78,164.8 -78,162.4 -78,160 -78,157.6 -78,155.2 -78,152.8 -78,150.4 -78,148 -78,148 -77.35,148 -76.7,148 -76.05,148 -75.4,148 -74.75,148 -74.1,148 -73.45,148 -72.8,148 -72.15,148 -71.5))"], "date_created": "Wed, 24 Jan 2024 00:00:00 GMT", "description": "As seismic data availability increases, the necessity for automated processing techniques has become increasingly evident. Expanded geophysical datasets collected over the past several decades across Antarctica provide excellent resources to evaluate different event detection approaches. We have used the traditional Short-Term Average/Long-Term Average (STA/LTA) algorithm to catalogue seismic data recorded by 19 stations in East Antarctica between 2012 and 2015. However, the complexities of the East Antarctic dataset, including low magnitude events and phenomena such as icequakes, warrant more advanced automated detection techniques. Therefore, we have also applied template matching as well as several deep learning algorithms, including Generalized Phase Detection (GPD), PhaseNet, BasicPhaseAE, and EQTransformer (EQT), to identify seismic phases within our dataset. Our goal was not only to increase the volume of detectable seismic events but also to gain insights into the effectiveness of these different automated approaches. Our assessment evaluated the completeness of the newly generated catalogs, the precision of identified event locations, and the quality of the picks. The final events corresponding to each of our three catalogs (based on STA/LTA, template matching, and machine learning, respectively) are listed in the provided files.", "east": 172.0, "geometry": ["POINT(160 -74.75)"], "keywords": "Antarctica; Geoscientificinformation; Machine Learning; Seismic Event Detection; Seismology; Seismometer", "locations": "Antarctica", "north": -71.5, "nsf_funding_programs": "Antarctic Earth Sciences", "persons": "Hansen, Samantha; Ho, Long; Walter, Jacob", "project_titles": "Collaborative Research: Resolving earth structure influence on ice-sheet stability in the Wilkes\r\nSubglacial Basin (RESISSt)", "projects": [{"proj_uid": "p0010204", "repository": "USAP-DC", "title": "Collaborative Research: Resolving earth structure influence on ice-sheet stability in the Wilkes\r\nSubglacial Basin (RESISSt)"}], "repo": "USAP-DC", "repositories": "USAP-DC", "science_programs": null, "south": -78.0, "title": "East Antarctic Seismicity from different Automated Event Detection Algorithms", "uid": "601762", "west": 148.0}]
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| Dataset Title/Abstract/Map | NSF Award(s) | Date Created | PIs / Scientists | Project Links | Abstract | Bounds Geometry | Geometry | Selected | Visible |
|---|---|---|---|---|---|---|---|---|---|
|
Surface melt-related multi-source remote-sensing and climate model data over Helheim Glacier, Greenland for segmentation and machine learning applications
|
2136938 |
2024-10-07 | Alexander, Patrick; Antwerpen, Raphael; Cervone, Guido; Fettweis, Xavier; Lütjens, Björn; Tedesco, Marco |
Collaborative Research: EAGER: Generation of high resolution surface melting maps over Antarctica using regional climate models, remote sensing and machine learning |
This dataset contains high-resolution satellite-derived snow/ice surface melt-related data on a common 100 m equal area grid (Albers equal area projection; EPSG 9822) over Helheim Glacier and surrounding areas in Greenland. The data is used as part of a machine learning framework that aims to fill data gaps in computed meltwater fraction on the 100 m grid using a range of methods, results of which will be published separately. <br/><br/> <br/><br/>The data include fraction of a grid cell covered by meltwater derived from Sentinel-1 synthetic aperture radar (SAR) backscatter, satellite-derived passive microwave (PMW) brightness temperatures, snowpack liquid water content within the first meter of snow and atmospheric and radiative variables from the Modéle Atmosphérique Règional (MAR) regional climate model, spectral reflectance in four wavelength bands from the Moderate Resolution Imaging Spectroradiometer (MODIS), a static digital elevation model (DEM), and an ice sheet mask. <br/><br/>A similar dataset has also been produced for Larsen C ice shelf and is also available through the US Antarctic Program Data Center. <br/><br/> <br/><br/> <br/><br/> | ["POLYGON((-40 67.55,-39.611 67.55,-39.222 67.55,-38.833 67.55,-38.444 67.55,-38.055 67.55,-37.666 67.55,-37.277 67.55,-36.888 67.55,-36.499 67.55,-36.11 67.55,-36.11 67.28999999999999,-36.11 67.03,-36.11 66.77,-36.11 66.51,-36.11 66.25,-36.11 65.99,-36.11 65.73,-36.11 65.47,-36.11 65.21000000000001,-36.11 64.95,-36.499 64.95,-36.888 64.95,-37.277 64.95,-37.666 64.95,-38.055 64.95,-38.444 64.95,-38.833 64.95,-39.222 64.95,-39.611 64.95,-40 64.95,-40 65.21000000000001,-40 65.47,-40 65.73,-40 65.99,-40 66.25,-40 66.51,-40 66.77,-40 67.03,-40 67.28999999999999,-40 67.55))"] | ["POINT(-38.055 66.25)"] | false | false |
|
Surface melt-related multi-source remote-sensing and climate model data over Larsen C Ice Shelf, Antarctica for segmentation and machine learning applications
|
2136938 |
2024-10-07 | Alexander, Patrick; Antwerpen, Raphael; Cervone, Guido; Fettweis, Xavier; Lütjens, Björn; Tedesco, Marco |
Collaborative Research: EAGER: Generation of high resolution surface melting maps over Antarctica using regional climate models, remote sensing and machine learning |
This dataset contains high-resolution satellite-derived snow/ice surface melt-related data on a common 100 m equal area grid (Lambert azimuthal equal area projection; EPSG 9820) over Larsen C Ice Shelf and surrounding areas in Antarctica. The data is prepared to be used as part of a machine learning framework that aims to fill data gaps in computed meltwater fraction on the 100 m grid using a range of methods, results of which will be published separately. <br/><br/><br/>The data include fraction of a grid cell covered by meltwater derived from Sentinel-1 synthetic aperture radar (SAR) backscatter, satellite-derived passive microwave (PMW) brightness temperatures, snowpack liquid water content within the first meter of snow and atmospheric and radiative variables from the Modéle Atmosphérique Règional (MAR) regional climate model, a static digital elevation model (DEM), and an ice sheet mask. <br/><br/><br/>A similar dataset has been produced for Helheim Glacier, Greenland and is also available through the US Antarctic Program Data Center. | ["POLYGON((-68.5 -65.25,-67.35 -65.25,-66.2 -65.25,-65.05 -65.25,-63.9 -65.25,-62.75 -65.25,-61.6 -65.25,-60.45 -65.25,-59.3 -65.25,-58.15 -65.25,-57 -65.25,-57 -65.652,-57 -66.054,-57 -66.456,-57 -66.858,-57 -67.25999999999999,-57 -67.66199999999999,-57 -68.064,-57 -68.466,-57 -68.868,-57 -69.27,-58.15 -69.27,-59.3 -69.27,-60.45 -69.27,-61.6 -69.27,-62.75 -69.27,-63.9 -69.27,-65.05 -69.27,-66.2 -69.27,-67.35 -69.27,-68.5 -69.27,-68.5 -68.868,-68.5 -68.466,-68.5 -68.064,-68.5 -67.66199999999999,-68.5 -67.25999999999999,-68.5 -66.858,-68.5 -66.456,-68.5 -66.054,-68.5 -65.652,-68.5 -65.25))"] | ["POINT(-62.75 -67.25999999999999)"] | false | false |
|
East Antarctic Seismicity from different Automated Event Detection Algorithms
|
1914698 |
2024-01-24 | Hansen, Samantha; Ho, Long; Walter, Jacob |
Collaborative Research: Resolving earth structure influence on ice-sheet stability in the Wilkes
Subglacial Basin (RESISSt) |
As seismic data availability increases, the necessity for automated processing techniques has become increasingly evident. Expanded geophysical datasets collected over the past several decades across Antarctica provide excellent resources to evaluate different event detection approaches. We have used the traditional Short-Term Average/Long-Term Average (STA/LTA) algorithm to catalogue seismic data recorded by 19 stations in East Antarctica between 2012 and 2015. However, the complexities of the East Antarctic dataset, including low magnitude events and phenomena such as icequakes, warrant more advanced automated detection techniques. Therefore, we have also applied template matching as well as several deep learning algorithms, including Generalized Phase Detection (GPD), PhaseNet, BasicPhaseAE, and EQTransformer (EQT), to identify seismic phases within our dataset. Our goal was not only to increase the volume of detectable seismic events but also to gain insights into the effectiveness of these different automated approaches. Our assessment evaluated the completeness of the newly generated catalogs, the precision of identified event locations, and the quality of the picks. The final events corresponding to each of our three catalogs (based on STA/LTA, template matching, and machine learning, respectively) are listed in the provided files. | ["POLYGON((148 -71.5,150.4 -71.5,152.8 -71.5,155.2 -71.5,157.6 -71.5,160 -71.5,162.4 -71.5,164.8 -71.5,167.2 -71.5,169.6 -71.5,172 -71.5,172 -72.15,172 -72.8,172 -73.45,172 -74.1,172 -74.75,172 -75.4,172 -76.05,172 -76.7,172 -77.35,172 -78,169.6 -78,167.2 -78,164.8 -78,162.4 -78,160 -78,157.6 -78,155.2 -78,152.8 -78,150.4 -78,148 -78,148 -77.35,148 -76.7,148 -76.05,148 -75.4,148 -74.75,148 -74.1,148 -73.45,148 -72.8,148 -72.15,148 -71.5))"] | ["POINT(160 -74.75)"] | false | false |
