Mahmoud Ayyad
Stevens Institute of Technology | Research Assistant Professor
| Subject Areas: | Hydrology, river ice |
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ABSTRACT:
A computer vision-based framework, River Ice-Network (RIce-Net), uses the USGS nationwide network of ground-based cameras whose images are published through the National Imagery Management System (NIMS), which is available to the public through the Hydrologic Imagery Visualization and Information System (HIVIS; https://apps.usgs.gov/hivis). RIce-Net classifies ice-affected images, segments the ice presence over river surface, calculates the fraction of ice coverage, and automatically generates a near real-time ice flag. The RIce-Net was trained using images from the Milwaukee River near Cedarburg station (https://apps.usgs.gov/hivis/camera/WI_Milwaukee_River_near_Cedarburg) collected in 2023 and tested using images collected in 2024. This repository provides the training data set of the classification and segmentation models.
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| Website | https://www.stevens.edu/profile/mayyad |
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Created: Jan. 26, 2025, 7:13 p.m.
Authors: Ayyad, Mahmoud · Temimi, Marouane · Abdelkader, Mohamed · Henein, Moheb
ABSTRACT:
A computer vision-based framework, River Ice-Network (RIce-Net), uses the USGS nationwide network of ground-based cameras whose images are published through the National Imagery Management System (NIMS), which is available to the public through the Hydrologic Imagery Visualization and Information System (HIVIS; https://apps.usgs.gov/hivis). RIce-Net classifies ice-affected images, segments the ice presence over river surface, calculates the fraction of ice coverage, and automatically generates a near real-time ice flag. The RIce-Net was trained using images from the Milwaukee River near Cedarburg station (https://apps.usgs.gov/hivis/camera/WI_Milwaukee_River_near_Cedarburg) collected in 2023 and tested using images collected in 2024. This repository provides the training data set of the classification and segmentation models.