{"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. \n\nThis dataset is associated with the following publication:\nIslam, 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": ["water quality", "bathymetry", "seagrass", "satellite", "coastal ocean"], "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"}