@prefix dcat: <http://www.w3.org/ns/dcat#> .
@prefix dct: <http://purl.org/dc/terms/> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix gsp: <http://www.opengis.net/ont/geosparql#> .
@prefix locn: <http://www.w3.org/ns/locn#> .
@prefix vcard: <http://www.w3.org/2006/vcard/ns#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

<https://data.amerigeoss.org/dataset/247595fd-de30-4ad2-bc00-eb7395e541a2> a dcat:Dataset ;
    dct:description "The data are a set of fluorescent images that were generated to support the development of a machine learning model.  The approach combines fluorescence imaging, deep learning, a mobile application, and a data management system for automated and real-time oil spill assessment. The dataset is comprised of 1,530 fluorescence images from two distinct oil types, a napthalenic crude oil (NACO) and an aromatic-napthalenic crude oil (ANCO). The oil is diluted in hexane and the images represent concentrations ranging from 0 to 500 mg/L. The data are presented as JPEG files in two zip folders (one for each oil type) as well as a CSV file that describes the type and concentration of the oil photographed in each image. These images were used to train and evaluate a machine learning tool comprised of convolutional neural network architecture for feature extraction coupled with a custom regression model. Model description and code can be found at https://github.com/biplabpoudel25/Oil-spill-estimation." ;
    dct:identifier <http://datainventory.doi.gov/id/dataset/usgs-689a01fdd4be02504d348c18> ;
    dct:issued "2025-09-24T11:06:08.623472"^^xsd:dateTime ;
    dct:modified "2025-08-15T00:00:00+00:00"^^xsd:dateTime ;
    dct:publisher <https://data.amerigeoss.org/organization/727dbdd5-3f98-4ac0-9d28-5e344558139b> ;
    dct:spatial [ a dct:Location ;
            locn:geometry "POLYGON ((46.8234 40.1858, 46.8234 40.5095, 51.0750 40.5095, 51.0750 40.1858, 46.8234 40.1858))"^^gsp:wktLiteral ] ;
    dct:title "Images of two standard crude oils collected using a fluorescent camera device to train and optimize a machine learning model for real-time oil spill concentration assessment collected from November 7, 2023, to July 8, 2024" ;
    dcat:contactPoint [ a vcard:Organization ;
            vcard:fn "Jeffery A. Steevens" ;
            vcard:hasEmail <mailto:jsteevens@usgs.gov> ] ;
    dcat:distribution <https://data.amerigeoss.org/dataset/247595fd-de30-4ad2-bc00-eb7395e541a2/resource/f40d0361-4c20-41a2-8aaa-4aebf6476730> ;
    dcat:keyword "AmeriGEO",
        "AmeriGEOSS",
        "CKAN",
        "GEO",
        "GEOSS",
        "National",
        "North America",
        "United States",
        "artificial-intelligence",
        "azerbaijan",
        "biota",
        "columbia-environmental-research-center",
        "image-analysis",
        "industrial-pollution",
        "machine-learning",
        "petroleum",
        "usgs-689a01fdd4be02504d348c18" ;
    dcat:theme <%7Bgeospatial%7D> .

<https://data.amerigeoss.org/dataset/247595fd-de30-4ad2-bc00-eb7395e541a2/resource/f40d0361-4c20-41a2-8aaa-4aebf6476730> a dcat:Distribution ;
    dct:description "Landing page for access to the data" ;
    dct:format "XML" ;
    dct:issued "2025-09-13T10:33:27.662653"^^xsd:dateTime ;
    dct:modified "2025-09-24T11:06:08.596086"^^xsd:dateTime ;
    dct:title "Digital Data" ;
    dcat:accessURL <https://doi.org/10.5066/P1SXVZX2> ;
    dcat:mediaType "application/http" .

<https://data.amerigeoss.org/organization/727dbdd5-3f98-4ac0-9d28-5e344558139b> a foaf:Agent ;
    foaf:name "US Migrating" .