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Ensemble Machine Learning Approaches for Bathymetry Estimation in Multi-spectral Images
Kazi Aminul Islam at Kennesaw State University is the owner of the analysis data. Contact the lead author at kislam4@kennesaw.edu. This dataset is not publicly accessible because: NGA Nextview and NASA Commercial Data Buy license agreements prohibit the distribution of original data files from WorldView due to copyright. It can be accessed through the following means: N/A. Format: Original data files from WorldView.
This dataset is associated with the following publication:
Islam, K., O. Abul-Hassan, H. Zhang, V. Hill, B. Schaeffer, R. Zimmerman, and J. Li. Ensemble Machine Learning Approaches for Bathymetry Estimation in Multi-Spectral Images. Geomatics. MDPI, Basel, SWITZERLAND, 5(3): 34, (2025).
Complete Metadata
| accessLevel | public |
|---|---|
| bureauCode |
[
"020:00"
]
|
| contactPoint |
{
"fn": "Blake Schaeffer",
"hasEmail": "mailto:schaeffer.blake@epa.gov"
}
|
| description | Kazi Aminul Islam at Kennesaw State University is the owner of the analysis data. Contact the lead author at kislam4@kennesaw.edu. This dataset is not publicly accessible because: NGA Nextview and NASA Commercial Data Buy license agreements prohibit the distribution of original data files from WorldView due to copyright. It can be accessed through the following means: N/A. Format: Original data files from WorldView. This dataset is associated with the following publication: Islam, K., O. Abul-Hassan, H. Zhang, V. Hill, B. Schaeffer, R. Zimmerman, and J. Li. Ensemble Machine Learning Approaches for Bathymetry Estimation in Multi-Spectral Images. Geomatics. MDPI, Basel, SWITZERLAND, 5(3): 34, (2025). |
| distribution |
[]
|
| identifier | https://doi.org/10.23719/1531724 |
| keyword |
[
"bathymetry",
"coastal ocean",
"satellite",
"seagrass",
"water quality"
]
|
| license | https://pasteur.epa.gov/license/sciencehub-license-non-epa-generated.html |
| modified | 2024-08-15 |
| programCode |
[
"020:000"
]
|
| publisher |
{
"name": "U.S. EPA Office of Research and Development (ORD)",
"subOrganizationOf": {
"name": "U.S. Environmental Protection Agency",
"subOrganizationOf": {
"name": "U.S. Government"
}
}
}
|
| references |
[
"https://doi.org/10.3390/geomatics5030034"
]
|
| rights |
null
|
| title | Ensemble Machine Learning Approaches for Bathymetry Estimation in Multi-spectral Images |
