An Active Learning Pipeline to Detect Hurricane Washover in Post-Storm Aerial Images

This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint.

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Authors

Evan B Goldstein , Somya D Mohanty , Shah Nafis Rafique, Jamison Valentine

Abstract

We present an active learning pipeline to identify hurricane impacts on coastal landscapes. Previously unlabeled post-storm images are used in a three component workflow — first an online interface is used to crowd-source labels for imagery; second, a convolutional neural network is trained using the labeled images; third, model predictions are displayed on an interactive map. Both the labeler and interactive map allow coastal scientists to provide additional labels that will be used to develop a large labeled dataset, a refined model, and improved hurricane impact assessments.

DOI

https://doi.org/10.31223/X5JW23

Subjects

Environmental Monitoring, Geomorphology, Physical Sciences and Mathematics

Keywords

Hurricane, Hurricane Impact

Dates

Published: 2020-12-02 22:09

Last Updated: 2020-12-02 22:09

License

CC0 1.0 Universal - Public Domain Dedication

Additional Metadata

Conflict of interest statement:
None

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