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Data from "Mapping bedrock outcrops in the Sierra Nevada Mountains (California, USA) using machine learning"

Metadata Updated: July 6, 2024

Accurate, high-resolution maps of bedrock outcrops are extremely valuable. The increasing availability of high-resolution imagery can be coupled with machine learning techniques to improve regional bedrock maps. This data release contains training data created for developing a machine learning model capable of identifying exposed bedrock across the entire Sierra Nevada Mountains (California, USA). The training data consist of 20 thematic rasters in GeoTIFF format, where image labels represent three categories: rock, not rock, and no data. These training data labels were created using 0.6-m imagery from the National Agriculture Imagery Program (NAIP) acquired in 2016. Eight existing labeled sites were available from Petliak et al. (2019), an earlier effort. We further revised those labels for improved accuracy and created additional 12 reference sites following the same protocol of semi-manual mapping in Petliak et al. (2019). A machine learning model (https://github.com/nasa/delta) was trained and tested based on these image labels as detailed in Shastry et al. (in review). The trained model was then used to map exposed bedrock across the entire Sierra Nevada region using 2016 NAIP imagery, and this data release also includes these model outputs. The model output gives the likelihood (from 0 to 255) that each pixel is bedrock, and not a direct binary classification. The associated publication used a threshold of 50%, or pixel value 127, where all pixel values 127 or higher are classified as rock and less than as not rock.

Access & Use Information

Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

Downloads & Resources

Dates

Metadata Created Date September 19, 2023
Metadata Updated Date July 6, 2024

Metadata Source

Harvested from DOI EDI

Additional Metadata

Resource Type Dataset
Metadata Created Date September 19, 2023
Metadata Updated Date July 6, 2024
Publisher U.S. Geological Survey
Maintainer
@Id http://datainventory.doi.gov/id/dataset/77257660becb2697e0d628feed66d99a
Identifier USGS:6489cf31d34ef77fcafe5b7c
Data Last Modified 20230913
Category geospatial
Public Access Level public
Bureau Code 010:12
Metadata Context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
Metadata Catalog ID https://datainventory.doi.gov/data.json
Schema Version https://project-open-data.cio.gov/v1.1/schema
Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
Harvest Object Id 9e7a4e96-ffce-43c3-8fa1-38c12a3c6ffd
Harvest Source Id 52bfcc16-6e15-478f-809a-b1bc76f1aeda
Harvest Source Title DOI EDI
Metadata Type geospatial
Old Spatial -120.9706,35.1716,-117.5858,39.7016
Publisher Hierarchy White House > U.S. Department of the Interior > U.S. Geological Survey
Source Datajson Identifier True
Source Hash 51d1af7a0ca54364de7a4a7c78c98edd89db8c061dcf8144b1459372598364d4
Source Schema Version 1.1
Spatial {"type": "Polygon", "coordinates": -120.9706, 35.1716, -120.9706, 39.7016, -117.5858, 39.7016, -117.5858, 35.1716, -120.9706, 35.1716}

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