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| Type: | Resource | |
| Storage: | The size of this resource is 406.4 MB | |
| Created: | Jul 11, 2025 at 11:47 p.m. (UTC) | |
| Last updated: | Aug 03, 2025 at 12:35 a.m. (UTC) | |
| Citation: | See how to cite this resource | |
| Content types: | CSV Content |
| Sharing Status: | Public |
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| Views: | 1224 |
| Downloads: | 46 |
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Abstract
This HydroShare resource provides a complete example of camera-based streamflow monitoring data collection and automated segmentation processing for the Blacksmith Fork site, demonstrated on one day of real image data. It includes both example image inputs and compute outputs generated using a containerized cloud-based inference pipeline. The processing workflow uses the Segment Anything deep learning model, deployed in a serverless environment with AWS Lambda and S3. Each image is segmented to identify regions of interest (ROIs) and calculate water-relevant pixel statistics. Ground truth comparison supports quality assurance using Intersection over Union (IoU) scores. Results are automatically uploaded and stored in a PostgreSQL database for hydrologic analysis. This dataset supports the reproducibility of the modeling approaches described in submitted manuscripts to Environmental Modelling & Software, offering transparency into the full data processing pipeline from raw image ingestion to output storage. It serves as a reference implementation for camera-based environmental monitoring at scale.
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Content
Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
|---|---|---|
| National Oceanic and Atmospheric Administration | Cooperative Institute for Research to Operations in Hydrology (CIROH) | NA22NWS4320003 |
| Utah Water Research Laboratory | None | None |
| United States Geological Survey | None | None |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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