Automated Extraction of Drainage Channels and Roads through Deep Learning

Stanislawski, Lawrence V.; Brockmeyer, Tyler; Shavers, Ethan

doi:10.5194/ica-abs-1-350-2019

Cited by 2 publications

(2 citation statements)

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“…Application of machine learning techniques, such as artificial neural networks, that are well-trained to identify feature patterns or mimic complex feature interactions is an attractive alternative that could furnish more accurate results through more consistently applied workflows over time and space. Recent work with machine learning has revealed promising results for extraction of hydrography [18][19][20][21][22][23] and other associated features [24][25][26] from lidar point cloud and other remotely sensed data. Research presented in this paper aims to test and develop machine learning workflows to extract hydrographic features from 3DEP and other data to enhance the collection and validation of hydrography data.…”

Section: Introductionmentioning

confidence: 99%

“…The work presented here capitalizes upon the IfSAR digital models by generating elevation-derived layers-such as, curvature, topographic position index, and geomorphons-that have been shown to reflect geomorphic conditions [12,[30][31][32]. Earlier work tested various iterations and parameters using object-based image analysis software to estimate the suitability of each layer for delineating waterbodies and streams [25,26,33]. The raster maps, or themes, found suitable are used as input data for U-net models.…”

Section: Introductionmentioning

confidence: 99%

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Extensibility of U-Net Neural Network Model for Hydrographic Feature Extraction and Implications for Hydrologic Modeling

et al. 2021

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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 NHD features are often digitized representations of historic topographic map blue lines and may be outdated.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 Alaska hydrographic databases. U-net artificial neural networks (ANN) and high-performance computing (HPC) are used for supervised hydrographic feature extraction within a study area comprised of 50 contiguous watersheds in Alaska. Surface water features derived from elevation through automated flow-routing and manual editing are used as training data. Model extensibility is tested with a series of 16 U-net models trained with increasing percentages of the study area, from about 3 to 35 percent. Hydrography is predicted by each of the models for all watersheds not used in training. Input raster layers are derived from digital terrain models, digital surface models, and intensity images from the IfSAR data. Results indicate about 15 percent of the study area is required to optimally train the ANN to extract hydrography when F1-scores for tested watersheds average between 66 and 68. Little benefit is gained by training beyond 15 percent of the study area. Fully connected hydrographic networks are generated for the U-net predictions using a novel approach that constrains a D-8 flow-routing approach to follow U-net predictions. This work demonstrates the ability of deep learning to derive surface water feature maps from complex terrain over a broad area.

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