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| Type: | Resource | |
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| Created: | Nov 03, 2025 at 7:39 p.m. (UTC) | |
| Last updated: | Jan 20, 2026 at 6:02 p.m. (UTC) (Metadata update) | |
| Published date: | Jan 20, 2026 at 6:02 p.m. (UTC) | |
| DOI: | 10.4211/hs.2aca7bf88a944b86af4cd4c4b01bed71 | |
| Citation: | See how to cite this resource | |
| Content types: | CSV Content |
| Sharing Status: | Published |
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| Views: | 126 |
| Downloads: | 63 |
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Abstract
The USGS operates a nationwide network of ground-based cameras, where the collected images are stored in the cloud-based National Imagery Management System (https://api.waterdata.usgs.gov/docs/nims) and made available to the public through the Hydrologic Imagery Visualization and Information System (https://apps.usgs.gov/hivis) These cameras are colocated with USGS streamgage stations. As of January 2025, the network comprises approximately 830 cameras strategically distributed across major watersheds in the contiguous U.S., Alaska, the Caribbean, and the South Pacific regions. The cameras in Alaska are exposed to harsh weather conditions, which frequently degrade images, particularly due to haze. This degradation leads to data loss over multiple days at certain stations, especially those with a daily image upload frequency. In total, 6,587 images were collected from nine stations between October 1st, 2022, and October 20th, 2024. The dataset comprises 6,405 haze-free, 105 hazy, and 77 corrupted images.
Subject Keywords
Coverage
Spatial
Temporal
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Content
README.md
Diffusion-Leveraged GAN Dehazing Driven by Classification: A Two-Stage Framework for Real-World Monitoring Imagery
Quick start: Getting Started | Get Data | Haze Classifier| Dehazing
Image Dehazing
| Input | Model Output |
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Haze Detection
We propose a framework, it consists of two primary components: an image classification module that is followed by an image dehazing module. The image classification module categorizes an input image as haze-free, hazy, or corrupted.
Then, the dehazing module restores the hazy images only, while the haze-free images are accepted and the corrupted images are rejected. The classification module improves the dehazing performance and the overall quality of river monitoring images while preventing unnecessary dehazing of haze-free and corrupted images.
Getting Started
Our dehazing model was developed using the provided code by Paper in Github repo.
Clone the img2img-turbo
git clone https://github.com/GaParmar/img2img-turbo.git
Navigate into the img2img-turbo folder and follow the environment setup provided by the authors as follows:
Environment Setup
- environment.yml contains all the required dependencies.
conda env create -f environment.yaml
- Following this, you can activate the conda environment with the command below.
conda activate img2img-turbo
- Or use virtual environment:
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
Get data
- Make sure that the HIVIS.csv exists.
- Run 0_Prepare_Data notebook first to fetch the data from HIVIS.
Haze Classifier
- Run 1_Classifier notebook to test the classification performance.
Dehazing
- Run 2_Dehazing notebook to test the dehazing performance using our trained model.
Acknowledgment
Our work uses CycleGAN-Turbo as the base model with the following LICENSE.
Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
|---|---|---|
| NOAA | None | NA22NWS4320003 |
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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