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.
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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) |
