Abstract
Precise coastal shoreline mapping is essential for monitoring changes in erosion rates, surface hydrology, and ecosystem structure and function. Monitoring water bodies in the Arctic National Wildlife Refuge (ANWR) is of high importance, especially considering the potential for oil and natural gas exploration in the region. In this work, we propose a modified variant of the Deep Neural Network based U-Net Architecture for the automated mapping of 4 Band Orthorectified NOAA Airborne Imagery using sparsely labeled training data and compare it to the performance of traditional Machine Learning (ML) based approaches—namely, random forest, xgboost—and spectral water indices—Normalized Difference Water Index (NDWI), and Normalized Difference Surface Water Index (NDSWI)—to support shoreline mapping of Arctic coastlines. We conclude that it is possible to modify the U-Net model to accept sparse labels as input and the results are comparable to other ML methods (an Intersection-over-Union (IoU) of 94.86% using U-Net vs. an IoU of 95.05% using the best performing method).
Funder
National Aeronautics and Space Administration
National Science Foundation
National Oceanic and Atmospheric Administration
Subject
General Earth and Planetary Sciences
Reference97 articles.
1. Collapsing arctic coastlines;Fritz;Nat. Clim. Chang.,2017
2. The catastrophic thermokarst lake drainage events of 2018 in northwestern Alaska: Fast-forward into the future;Nitze;Cryosphere,2020
3. Arctic-COLORS (Coastal Land Ocean Interactions in the Arctic)—A NASA field campaign scoping study to examine land-ocean interactions in the Arctic;Hernes,2014
4. State of the Arctic Coast 2010: Scientific Review and Outlook;Forbes,2011
Cited by
12 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献