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Deep learning model to extract water bodies from Sentinel-1data. A brief summary of the item is not available. Add a brief summary about the item.

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Item created: Sep 15, 2022 Item updated: Jan 1, 2025 Number of downloads: 14,021

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Description

Water is an indispensable resource not only for humans but for all living being on earth. Conservation and management of water resources helps sustain and thrive life and also prevent its destruction. Water management can include activities such as monitoring the changing course of rivers and streams, regional planning, flood management, agriculture, and so on, all of which requires survey and planning, including accurate mapping of water bodies. Hence, extraction of water bodies from remote sensing data is critical to record how this dynamic changes and map their current forms. The remote sensing data used here is SAR, which is a powerful imagery for information extraction, as it is unaffected by cloud cover, acquires images overnight, enables all-weather imaging, and it is cost effective compared to other imageries. This deep learning model can be used to automate the task of extracting water bodies from SAR imagery.

Using the model

Follow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.


Fine-tuning the model
This model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.

Input

8-bit, 3-band Sentinel-1 C band SAR GRD VH polarization band raster.

Output
Binary raster representing water and non-water classes

Applicable geographies

The model is expected to work well in the United States.

Model architecture

The model uses the DeepLab model architecture implemented in ArcGIS API for Python.

Accuracy metrics

The model has a precision of 0.945, recall of 0.92 and F1-score of 0.933.

Training data
This model is trained on manually classified training dataset. Labels were created by using Sentinel-1 C band SAR GRD VH polarization imagery using histogram based thresholding method, followed by QA and manual cleaning to get water masks.

Sample results
Here are few results from the model.







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