2021
DOI: 10.3390/rs13122368
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Extensibility of U-Net Neural Network Model for Hydrographic Feature Extraction and Implications for Hydrologic Modeling

Abstract: Accurate maps of regional surface water features are integral for advancing ecologic, atmospheric and land development studies. The only comprehensive surface water feature map of Alaska is the National Hydrography Dataset (NHD). NHD features are often digitized representations of historic topographic map blue lines and may be outdated. Here we test deep learning methods to automatically extract surface water features from airborne interferometric synthetic aperture radar (IfSAR) data to update and validate Al… Show more

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Cited by 19 publications
(19 citation statements)
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“…A CNN model based on U-Net (Ronneberger, Fisher and Brox 2015) was developed using the PyTorch 3 Python library. U-Net was chosen as base as it has been used for hydrographic feature extraction from DEMs before (e.g., Stanislawski et al 2021).…”
Section: Methodsmentioning
confidence: 99%
“…A CNN model based on U-Net (Ronneberger, Fisher and Brox 2015) was developed using the PyTorch 3 Python library. U-Net was chosen as base as it has been used for hydrographic feature extraction from DEMs before (e.g., Stanislawski et al 2021).…”
Section: Methodsmentioning
confidence: 99%
“…Since they were initially proposed [29], U-nets have become a staple in many scientific fields in the task of class segmentation of complex tissues [30], cellular components [31], satellite image processing [32] and even geological studies [33].…”
Section: Proposed Architecturementioning
confidence: 99%
“…An example of extending reference hydrographic data to extract additional features for the surrounding area using DL is shown in Figure 1. Methods are also being tested for fully automated extraction of hydrography from elevation by controlling flow accumulation models through DL (Stanislawski et al, 2021). HPC systems provide the computing power to rapidly generate or derive input data layers and test diverse strategies at speeds that make such testing viable.…”
Section: Hydrographic Feature Extraction and Modelingmentioning
confidence: 99%