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Title: An effective online platform for crowd classification of coastal wetland loss
Abstract

Wetland loss is increasing rapidly, and there are gaps in public awareness of the problem. By crowdsourcing image analysis of wetland morphology, academic and government studies could be supplemented and accelerated while engaging and educating the public. The Land Loss Lookout (LLL) project crowdsourced mapping of wetland morphology associated with wetland loss and restoration. We demonstrate that volunteers can be trained relatively easily online to identify characteristic wetland morphologies, or patterns present on the landscape that suggest a specific geomorphological process. Results from a case study in coastal Louisiana revealed strong agreement among nonexpert and expert assessments who agreed on classifications at least 83% and at most 94% of the time. Participants self‐reported increased knowledge of wetland loss after participating in the project. Crowd‐identified morphologies are consistent with expectations, although more work is needed to directly compare LLL results with previous studies. This work provides a foundation for using crowd‐based wetland loss analysis to increase public awareness of the issue, and to contribute to land surveys or train machine learning algorithms.

 
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Award ID(s):
1816426
NSF-PAR ID:
10390087
Author(s) / Creator(s):
 ;  ;  ;  ;  ;  ;  ;  
Publisher / Repository:
Wiley-Blackwell
Date Published:
Journal Name:
Conservation Science and Practice
Volume:
5
Issue:
1
ISSN:
2578-4854
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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