Data:AI&ML Challenge Dataset: Difference between revisions
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|Extended data description=Data for use with ML, dealing with the sediment/rock substrates of the NE USA Continental Margin. Training data from seabed observations should be spatially extended over the entire area in an intelligent way. To aid that environmental Feature Layers are employed to train various Machine Learning methods on the sample data then the results are extended across all the vacant areas. The result predicts what the seabed is made of, so that survey operations (including research) can be planned, or biogeochemical budgets can be calculated. The idea of the Challenge Dataset is to permit people - researchers and students - to experiment with various Machine Learning algorithms and data preparation adjustments to achieve the BEST possible mapping over the area. The mappings are in terms of mud/sand/gravel, exposed rock, carbonate, organic carbon, but other parameters are possible also. See the PPT file in the Zipfile for further instructions. | |Extended data description=Data for use with ML, dealing with the sediment/rock substrates of the NE USA Continental Margin. Training data from seabed observations should be spatially extended over the entire area in an intelligent way. To aid that environmental Feature Layers are employed to train various Machine Learning methods on the sample data then the results are extended across all the vacant areas. The result predicts what the seabed is made of, so that survey operations (including research) can be planned, or biogeochemical budgets can be calculated. The idea of the Challenge Dataset is to permit people - researchers and students - to experiment with various Machine Learning algorithms and data preparation adjustments to achieve the BEST possible mapping over the area. The mappings are in terms of mud/sand/gravel, exposed rock, carbonate, organic carbon, but other parameters are possible also. See the PPT file in the Zipfile for further instructions. | ||
|Upload image dataset=seabedStack v01.png | |Upload image dataset=seabedStack v01.png | ||
|Caption dataset image=Example Challenge Predictands | |Caption dataset image=Example Challenge Predictands | ||
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{{Data format | {{Data format | ||
Revision as of 15:19, 30 May 2019
AI&ML Challenge Dataset dataset information page
Short Description
 |
| Example Challenge Predictands |
Statement: Machine Learning 'Challenge Dataset' for the Seabed
Abstract: Data for use with ML, dealing with the sediment/rock substrates of the NE USA Continental Margin. Training data from seabed observations should be spatially extended over the entire area in an intelligent way. To aid that environmental Feature Layers are employed to train various Machine Learning methods on the sample data then the results are extended across all the vacant areas. The result predicts what the seabed is made of, so that survey operations (including research) can be planned, or biogeochemical budgets can be calculated. The idea of the Challenge Dataset is to permit people - researchers and students - to experiment with various Machine Learning algorithms and data preparation adjustments to achieve the BEST possible mapping over the area. The mappings are in terms of mud/sand/gravel, exposed rock, carbonate, organic carbon, but other parameters are possible also. See the PPT file in the Zipfile for further instructions.
Data format
| Data type: | Substrates |
| Data origin: | Measured |
| Data format: | |
| Other format: | zip |
| Data resolution: | ~1km |
| Datum: | WGS84 |
Data Coverage
Spatial data coverage: NE USA Continental Margin
Temporal data coverage: Time averaged
Time period covered: Post 1930
Availability
Download data: http://instaar.colorado.edu/~jenkinsc/CSDMS AI&ML/DataChallenge/DataChallenge 4AI&ML.zip
Data source: http://instaar.colorado.edu/~jenkinsc
