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
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.
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[
"010:12"
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|
| contactPoint |
{
"fn": "Jeffery A. Steevens",
"@type": "vcard:Contact",
"hasEmail": "mailto:jsteevens@usgs.gov"
}
|
| 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. |
| distribution |
[
{
"@type": "dcat:Distribution",
"title": "Digital Data",
"format": "XML",
"accessURL": "https://doi.org/10.5066/P1SXVZX2",
"mediaType": "application/http",
"description": "Landing page for access to the data"
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"@type": "dcat:Distribution",
"title": "Original Metadata",
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"downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.689a01fdd4be02504d348c18.xml"
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|
| identifier | http://datainventory.doi.gov/id/dataset/USGS_689a01fdd4be02504d348c18 |
| keyword |
[
"Azerbaijan",
"Columbia Environmental Research Center",
"Industrial pollution",
"USGS:689a01fdd4be02504d348c18",
"artificial intelligence",
"biota",
"image analysis",
"machine learning",
"petroleum"
]
|
| modified | 2025-08-15T00:00:00Z |
| publisher |
{
"name": "U.S. Geological Survey",
"@type": "org:Organization"
}
|
| spatial | 46.8234, 40.1858, 51.0750, 40.5095 |
| theme |
[
"geospatial"
]
|
| 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 |
