Large-scale asynchronous distributed learning based on parameter exchanges

Joshi, Bikash; Iutzeler, Franck; Amini, Massih-Reza

doi:10.1007/s41060-018-0110-5

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…We then describe the application of these systems to remote sensing data of the selected river stretches (Section 2.3). All processing was done in R using the terra and insol libraries, with the exception of the CNN, which was run in python 3.9.7 using TensorFlow 2.9.1 (Joshi et al, 2018) with Keras 2.9.0.…”

Section: Methodsmentioning

confidence: 99%

Automated fluvial hydromorphology mapping from airborne remote sensing

Hedger

Gosselin

2023

River Research & Apps

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Mapping fluvial hydromorphology is an important part of defining river habitat. Mapping via field sampling or hydraulic modeling is however time consuming, and mapping hydromorphology directly from remote sensing data may offer an efficient solution. Here, we present a system for automated classification of fluvial hydromorphology based on a deep learning classification scheme applied to aerial orthophotos. Using selected rivers in Norway, we show how surface flow patterns (smooth or rippled surfaces vs. standing waves) can be classified in imagery using a trained convolutional neural network (achieving a training and validation accuracy of >95%). We show how integration of these classified surface flow patterns with information on channel gradient, obtained from airborne topographic LiDAR data, can be used to compartmentalize the rivers into hydromorphological units (HMUs) that represent the dominant flow features. Automated classifications were broadly consistent with manual classifications that had been made in previous ground surveys, with equivalency in automated and manually derived HMU classes ranging from 61.5% to 87.7%, depending on the river stretch considered. They were found to be discharge‐dependent, showing the temporally dynamic aspect of hydromorphology. The proposed system is quick, flexible, generalizable, and provides consistent classifications free from interpretation bias. The deep learning approach used here can be customized to provide more detailed information on flow features, such as delineating between standing waves and advective diffusion of air bubbles/foam, to provide a more refined classification of surface flow patterns, and the classification approach can be further advanced by inclusion of additional remote sensing methods that provide further information on hydromorphological features.

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Section: Methodsmentioning

confidence: 99%

Automated fluvial hydromorphology mapping from airborne remote sensing

Hedger

Gosselin

2023

River Research & Apps

View full text Add to dashboard Cite

show abstract

“…We then describe the application of these systems to remote sensing data of the selected river stretches (Section 2.3). All processing was done in R using theterra and insol libraries, with the exception of the CNN, which was run in python 3.9.7 using TensorFlow 2.9.1 (Joshi et al 2018) with Keras 2.9.0.…”

Section: Methodsmentioning

confidence: 99%

Automated fluvial hydromorphology mapping from airborne remote sensing

Hedger

Gosselin

2023

Preprint

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Mapping fluvial hydromorphology is an important part of defining river habitat. Mapping via field sampling or hydraulic modelling is however time consuming, and mapping hydromorphology directly from remote sensing data may offer an efficient solution. Here we present a system for automated classification of fluvial hydromorphology based on a deep learning classification scheme applied to aerial orthophotos. Using selected rivers in Norway, we show how surface flow patterns (smooth or rippled surfaces versus standing waves) can be classified in imagery using a trained convolutional neural network. We show how integration of these classified surface flow patterns with information on channel gradient, obtained from airborne topographic LiDAR data, can be used to compartmentalize the rivers into hydromorphological units that represent the dominant flow features. Automated classifications were consistent with those produced manually. They were found to be discharge-dependent, showing the temporally dynamic aspect of hydromorphology. The proposed system is quick, flexible, generalizable, and free from researcher-subjectivity. The deep learning approach used here can be customized to provide more detailed information on flow features, such as delineating between standing waves and advective diffusion of air bubbles/foam, to provide a more refined classification of surface flow patterns, and the classification approach can be further advanced by inclusion of additional remote sensing methods that provide further information on hydromorphological features.

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