Machine learning with satellite imagery to document the historical transition from topographic to dense sub-surface agricultural drainage networks (tile drains)
Image library of (1) tile-drained landscapes and (2) tile-drain types used for training a machine-learning model that identifies (1) tile-drained landscapes and (2) differentiates two types of tile-drained areas visible in satellite imagery. These images were sourced from WorldView, Quickbird, and GeoEye satellite imagery (copyright DigitalGlobe) and cropped to features of interest. Imagery has a ground resolution of 0.34 - 0.65 m.
Complete Metadata
| accessLevel | public |
|---|---|
| bureauCode |
[
"010:12"
]
|
| contactPoint |
{
"fn": "Tanja N Williamson",
"@type": "vcard:Contact",
"hasEmail": "mailto:tnwillia@usgs.gov"
}
|
| description | Image library of (1) tile-drained landscapes and (2) tile-drain types used for training a machine-learning model that identifies (1) tile-drained landscapes and (2) differentiates two types of tile-drained areas visible in satellite imagery. These images were sourced from WorldView, Quickbird, and GeoEye satellite imagery (copyright DigitalGlobe) and cropped to features of interest. Imagery has a ground resolution of 0.34 - 0.65 m. |
| distribution |
[
{
"@type": "dcat:Distribution",
"title": "Digital Data",
"format": "XML",
"accessURL": "https://doi.org/10.5066/P9KSZ382",
"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.5e8c855e82cee42d13465d00.xml"
}
]
|
| identifier | http://datainventory.doi.gov/id/dataset/USGS_5e8c855e82cee42d13465d00 |
| keyword |
[
"Anthropocene",
"Great Lakes",
"Holocene",
"USGS:5e8c855e82cee42d13465d00",
"Western Lake Erie",
"agriculture"
]
|
| modified | 2023-06-01T00:00:00Z |
| publisher |
{
"name": "U.S. Geological Survey",
"@type": "org:Organization"
}
|
| spatial | -88.26416015631, 40.218812379331, -82.902832031523, 44.627692727744 |
| theme |
[
"Geospatial"
]
|
| title | Machine learning with satellite imagery to document the historical transition from topographic to dense sub-surface agricultural drainage networks (tile drains) |
