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Dataset for: Howard Slough Waterfowl Management Area Multispectral Imagery at Various Resolutions and Convolutional Neural Network Training Data

Description:

This dataset contains the materials necessary to reproduce the study submitted to Remote Sensing: "Tradeoffs Between UAS Spatial Resolution and Accuracy for Deep Learning Semantic Segmentation Applied to Wetland Vegetation Species Mapping". This includes the raw imagery output from the camera aboard the unoccupied aerial vehicle, the Red-Edge MX, captured over the Howard Slough Waterfowl Management Area, Utah, in August of 2020, resampled images, code to resample the images, a link to ground reference data, and the training and testing data used for the convolutional neural network in the study.

Keyword:

invasive species, wetlands, phragmites australis, Howard Slough Waterfowl Management Area, multispectral imagery, remote sensing, and uncrewed aerial systems

Subject:

geography

Creator:

Saltiel, Troy M., Campbell, Michael J., Thompson, Thomas R., Hambrecht, Keith R., and Dennison, Philip E.

Contributor:

Christian Hardwick (Utah Geological Survey)

Owner:

Based Near Label Tesim:

Howard Slough Waterfowl Management Area, Utah, United States

Language:

English

Date Uploaded:

04/21/2022

Date Modified:

12/05/2023

Date Created:

2020-08-11

License:

CC BY NC - Allows others to use and share your data non-commercially and with attribution.

Resource Type:

Dataset

Identifier:

https://doi.org/10.7278/S50d-h9z0-5ft8

Dataset for: Howard Slough Waterfowl Management Area Multispectral Imagery at Various Resolutions and Convolutional Neural Network Training Data

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