Data:AI&ML Challenge Dataset: Difference between revisions

Revision as of 11:13, 21 May 2019

AI&ML Challenge Dataset dataset information page

Short Description

CSDMS AI&ML Initative

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, carbonate, organic carbon, but other parameters are possible also.

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

References

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 {{Data description
 |One-line data description=Machine Learning Challenge Dataset for the Seabed
-|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.
+|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, carbonate, organic carbon, but other parameters are possible also.
 |Upload image dataset=AI&ML Logo.png
 |Caption dataset image=CSDMS AI&ML Initative