Deep learning for coastal resource conservation: automating detection of shellfish reefs-Reference-Cited by-同舟云学术

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

Published:2020-02-25 Issue:4 Volume:6 Page:431-440
ISSN:2056-3485
Container-title:Remote Sensing in Ecology and Conservation
language:en
Short-container-title:Remote Sens. Ecol.

Author:

Ridge Justin T.¹^ORCID,Gray Patrick C.¹^ORCID,Windle Anna E.²,Johnston David W.¹^ORCID

Affiliation:

1. Division of Marine Science and Conservation Nicholas School of the Environment Duke University Marine Laboratory 135 Duke Marine Lab Rd. Beaufort North Carolina 28516

2. University of Maryland Center for Environmental Science Horn Point Laboratory 2020 Horns Point Rd Cambridge Maryland21613

Funder

North Carolina Division of Marine Fisheries

North Carolina Sea Grant/Space Grant Fellowship program

Duke Bass Connections program

Microsoft AI for Earth program

Publisher

Wiley

Subject

Nature and Landscape Conservation,Computers in Earth Sciences,Ecology,Ecology, Evolution, Behavior and Systematics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/rse2.134

Reference35 articles.

1. The value of estuarine and coastal ecosystem services

2. Oyster Reefs at Risk and Recommendations for Conservation, Restoration, and Management

3. Automated detection of bird roosts using NEXRAD radar data and Convolutional Neural Networks

4. Detection of oyster habitat in tidal flats using multi-frequency polarimetric SAR data

5. Remote sensing for mapping natural habitats and their conservation status – New opportunities and challenges

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