Human‐in‐the‐Loop Segmentation of Earth Surface Imagery-Reference-Cited by-同舟云学术

Human‐in‐the‐Loop Segmentation of Earth Surface Imagery

Published:2022-03 Issue:3 Volume:9 Page:
ISSN:2333-5084
Container-title:Earth and Space Science
language:en
Short-container-title:Earth and Space Science

Author:

Buscombe D.¹^ORCID,Goldstein E. B.²^ORCID,Sherwood C. R.³^ORCID,Bodine C.⁴^ORCID,Brown J. A.⁵^ORCID,Favela J.⁶^ORCID,Fitzpatrick S.⁷^ORCID,Kranenburg C. J.⁸^ORCID,Over J. R.³^ORCID,Ritchie A. C.⁹^ORCID,Warrick J. A.⁹^ORCID,Wernette P.⁹^ORCID

Affiliation:

1. Marda Science LLC Contracted to USGS Pacific Coastal and Marine Science Center Santa Cruz CA USA

2. Department of Geography, Environment, and Sustainability University of North Carolina at Greensboro Greensboro NC USA

3. USGS Woods Hole Coastal and Marine Science Center Woods Hole MA USA

4. School of Informatics, Computing and Cyber Systems Northern Arizona University Flagstaff AZ USA

5. USGS MD‐DE‐DC Water Science Center Dover DE USA

6. Department of Earth and Planetary Sciences University of California Santa Cruz Santa Cruz CA USA

7. Department of Computer Science California State University Sacramento CA USA

8. USGS St. Petersburg Coastal and Marine Science Center St. Petersburg FL USA

9. USGS Pacific Coastal and Marine Science Center Santa Cruz CA USA

Funder

U.S. Geological Survey

U.S. Fish and Wildlife Service

Publisher

American Geophysical Union (AGU)

Subject

General Earth and Planetary Sciences,Environmental Science (miscellaneous)

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021EA002085

Reference114 articles.

1. Segmentation optimization and stratified object-based analysis for semi-automated geomorphological mapping

2. High Spatial Resolution Satellite Imagery, DEM Derivatives, and Image Segmentation for the Detection of Mass Wasting Processes

3. Multiple climate change-driven tipping points for coastal systems

4. Automatic detection of woody vegetation in repeat landscape photographs using a convolutional neural network

5. Machine learning for data-driven discovery in solid Earth geoscience

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