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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" ] |
| 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" }, { "@type": "dcat:Distribution", "title": "Original Metadata", "format": "XML", "mediaType": "text/xml", "description": "The metadata original format", "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.689a01fdd4be02504d348c18.xml" } ] |
| 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 |
