Machine learning with satellite imagery to document the historical transition from topographic to dense sub-surface agricultural drainage networks (tile drains)
May 31, 2023
Image library of (1) tile-drained landscapes and (2) tile-drain types that will be used for a machine-learning model workflow 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 and Quickbird satellite imagery (copyright DigitalGlobe) and cropped to features of interest. Imagery has a ground resolution of 0.34 - 0.65 m.
Citation Information
| Publication Year | 2023 |
|---|---|
| Title | Machine learning with satellite imagery to document the historical transition from topographic to dense sub-surface agricultural drainage networks (tile drains) |
| DOI | 10.5066/P9KSZ382 |
| Authors | Tanja N Williamson, Dayle J Hoefling |
| Product Type | Data Release |
| Record Source | USGS Digital Object Identifier Catalog |
| USGS Organization | Ohio-Kentucky-Indiana Water Science Center |
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