The 'Digital Grain Size' Web and Mobile-Computing Application

Citation

Langseth, M.L., Chang, M.Y., Carlino, Jennifer, Bellmore, J.R., Birch, D.D., Bradley, Joshua, Bristol, R.S., Buscombe, D.D., Duda, J.J., Everette, A.L., Graves, T.A., Greenwood, M.M., Govoni, H.S., Henkel, H.S., Hutchison, V.B., Jones, B.K., Kern, Tim, Lacey, Jennifer, Lamb, R.M., Lightsom, F.L., Long, J.L., Saleh, R.A., Smith, S.W., Soulard, C.E., Viger, R.J., Warrick, J.A., Wesenberg, K.E., Wieferich, D.J., and Winslow, L.A., 2016, Community for Data Integration 2015 annual report: U.S. Geological Survey Open-File Report 2016–1165, 57 p., http://dx.doi.org/10.3133/ofr20161165.

Summary

This project team developed a Web-hosted application (that can also be used on mobile platforms) for automatic analysis of images of sediment for grain-size distribution, using the “Digital Grain Size” (DGS) algorithm of Buscombe (2013) (“DGS-Online,” 2015). This is a free, browser-based application for accurately estimating the grain-size distribution of sediment in digital images without any manual intervention or even calibration. It uses the statistical algorithm of Buscombe (2013) that estimates particle size directly from the spatial distribution of light intensity within the image. The application is designed to batch-process tens to thousands of images, utilizing cloud computing storage and processing technologies. Typical [...]

Summary

This project team developed a Web-hosted application (that can also be used on mobile platforms) for automatic analysis of images of sediment for grain-size distribution, using the “Digital Grain Size” (DGS) algorithm of Buscombe (2013) (“DGS-Online,” 2015). This is a free, browser-based application for accurately estimating the grain-size distribution of sediment in digital images without any manual intervention or even calibration. It uses the statistical algorithm of Buscombe (2013) that estimates particle size directly from the spatial distribution of light intensity within the image. The application is designed to batch-process tens to thousands of images, utilizing cloud computing storage and processing technologies. Typical processing times are 1–60 seconds per image, depending on the size of file and the user inputs.

At the Web site, the user can (1) login to their personal dashboard using a Google account (gmail address); (2) create a “job” which involves uploading sediment imagery and assign “tags” and processing options to each image separately, to groups of images, or to all images at once; (3) launch a Web application to upload and process imagery on a cloud server; (4) and download grain-size distributions and other statistics in three formats (csv, xml, and json); and (5) create graphs of their results. The user will be able to store their imagery on the server for reprocessing images and redownloading data. The user can create as many separate jobs as they wish, privately and securely on their own personal dashboard, from which they can share results through their browser, securely, with whomever they wish.

Accomplishments

pyDGS Program.—A streamlined and faster version of the pyDGS program (version 3.0.0). This version of the code is running on the Web application, and is also publicly available through GitHub (Buscombe, 2015a) and python package index (Buscombe, 2015b).
DGS-Online.—A scalable Web application running node.js and python 2.7 on the Heroku platform, utilizing Amazon Web services simple queue service, simple storage service data storage, DynamoDB NoSQL cloud database, and Amazon Elastic compute cloud technologies (fig. 19). The application is scalable if the tool proves to be very popular.
The DGS-Online Web site (https://www.digitalgrainsize.org - content no longer available).—This is where users launch and interact with the program and read Web pages related to all aspects of the project, including (1) rationale, (2) history of algorithm development, (3) program user guide, (4) detailed information on how the algorithm works, (5) comprehensive FAQs, and (6) advice and best practices on image collection (fig. 20). As of October 30, 2015, the documentation is almost complete, and the Web site functionality is almost complete, with finishing work still in progress on graphics, design, logo, and domain name.

Note: This description is from the Community for Data Integration 2015 Annual Report.

Contacts

Principal Investigator :: Daniel D Buscombe
Cooperator/Partner :: Scott A Wright, Joseph Wheaton, Philip Bailey, David Rubin
CMS Group :: Community for Data Integration - CDI

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Digital Grain Size.PNG	2.23 MB	image/png
figure19.png “Schematic of the general computing workflow employed by DGS-Online ”	51.52 KB	image/png
figure20.jpg “Screenshots of the fully-functioning prototype DGS-Online Web application”	1.05 MB	image/jpeg

Related External Resources

Type: Web Application

DGS-Online: Grain Size Calculator from Images of Sediment - Content no longer available	https://www.digitalgrainsize.org/

Type: Citation

Buscombe, D., 2013, Transferable wavelet method for grain-size distribution from images of sediment surfaces and thin sections, and other natural granular patterns: Sedimentology 60, p. 1709–1732, accessed October 28, 2016 at	https://dx.doi.org/10.1111/sed.12049
DGS-Online—Grain size calculator from images of sediment, 2015, accessed March 25, 2016 - Content no longer available	https://www.digitalgrainsize.org/
Buscombe, D., 2015a, pyDGS—Wavelet-based digital grain size analysis: GitHub, accessed December 2, 2015, at	https://github.com/dbuscombe-usgs/pyDGS
Buscombe, D., 2015b, pyDGS 3.0.0—Wavelet-based digital grain size analysis: Python, accessed December 2, 2015, at	https://pypi.python.org/pypi/pyDGS/3.0.0

Type: Source Code

pyDGS Code Repository	https://github.com/dbuscombe-usgs/pyDGS
Published version of pyDGS Code Repository	https://zenodo.org/record/30571#.Wh2oplWnHmE

Project Extension

projectStatus	Completed

The 'Digital Grain Size' Web and Mobile-Computing Application

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