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.

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

Machine learning with satellite imagery to document the historical transition from topographic to dense sub-surface agricultural drainage networks (tile drains)

Research Hydrologist

Physical Scientist

Research Hydrologist

Physical Scientist

U.S. Geological Survey

U.S. Department of the Interior

Machine learning with satellite imagery to document the historical transition from topographic to dense sub-surface agricultural drainage networks (tile drains)

Citation Information

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Tanja N. Williamson, PhD

Research Hydrologist

Dayle Jordan Hoefling

Physical Scientist

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Tanja N. Williamson, PhD

Research Hydrologist

Dayle Jordan Hoefling

Physical Scientist