Data from: Deep learning for coastal resource conservation: automating detection of shellfish reefs

Public

  • 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 delineations of just 8%, attributable in part to OysterNet’s ability to detect oysters overlooked during manual demarcation. Further training of OysterNet could enable assessments of oyster reef heights and densities, and incorporation of more coastal habitat types. Future iterations will be applied to high-resolution satellite data for effective management at larger scales. ... [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
Publication Date
ARK
  • ark:/87924/r4cv4gx0h
Collection Dates
  • 2017-2018
Language
Type
Format
Related Materials
Funding Agency
  • North Carolina Department of Environmental Quality
Grant Number
  • #2017-H-068
Contact
Title
  • Data from: Deep learning for coastal resource conservation: automating detection of shellfish reefs
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