- It is increasingly important to understand the extent and health of coastal natural resources in the face of anthropogenic and climate-driven changes. Coastal ecosystems are difficult to efficiently monitor due to the inability of existing remotely-sensed data to capture complex spatial habitat patterns. To help managers and researchers avoid inefficient traditional mapping efforts, we developed a deep learning tool (OysterNet) that uses unoccupied aircraft systems (UAS) imagery to automatically detect and delineate oyster reefs, an ecosystem that has proven problematic to monitor remotely. OysterNet is a convolutional neural network (CNN) that assesses intertidal oyster reef extent, yielding a difference in total area between manual and automated ... [Read More]
- Total Size
- 8 files (27.6 GB)
- Data Citation
- Ridge, J. T., Gray, P. C., Windle, A. E., & Johnston, D. W. (2020), Deep learning for coastal resource conservation: automating detection of shellfish reefs. Remote Sens Ecol Conserv. doi:10.1002/rse2.134
- DOI
- 10.7924/r4cv4gx0h
- Subject
- Publication Date
- November 6, 2019
- ARK
- ark:/87924/r4cv4gx0h
- Affiliation
- Publisher
- Collection Dates
- 2017-2018
- Language
- Type
- Related Materials
- Funding Agency
- North Carolina Department of Environmental Quality
- Grant Number
- #2017-H-068
- Contact
- Patrick C. Gray; patrick.c.gray@duke.edu, ORCID: 0000-0002-8997-5255
- Title
- Data from: Deep learning for coastal resource conservation: automating detection of shellfish reefs
- Repository
Thumbnail | Title | Date Uploaded | Visibility | Actions |
---|---|---|---|---|
README.md | 2019-11-06 | Download | ||
180522BirdSEO_WGS84_transparent_mosaic_group1.tif | 2019-11-06 | |||
180605CarrotEO_transparent_mosaic_group1.tif | 2019-11-06 | |||
1kx1k_dataset.zip | 2019-11-06 | Download | ||
2kx2k_dataset.zip | 2019-11-06 | |||
4kx4k_dataset.zip | 2019-11-06 | |||
middlemarshrtk22feb2017_transparent_mosaic_group1.tif | 2019-11-06 | |||
shapefiles.zip | 2019-11-06 | Download |