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https://github.com/sefatanam/floodzonesnapshot

Harness the power of Google Earth Engine to effortlessly generate detailed flood susceptibility maps and images. Empowering researchers and responders to assess and mitigate flood risks with precision. Your go-to resource for informed decision-making in flood-prone areas
https://github.com/sefatanam/floodzonesnapshot

analysis flood-susceptibility google google-drive google-earth-engine image-processing map

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Harness the power of Google Earth Engine to effortlessly generate detailed flood susceptibility maps and images. Empowering researchers and responders to assess and mitigate flood risks with precision. Your go-to resource for informed decision-making in flood-prone areas

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README

        

# Flood susceptibility image export
Harness the power of Google Earth Engine to effortlessly generate detailed flood susceptibility maps and images. Empowering researchers and responders to assess and mitigate flood risks with precision. Your go-to resource for informed decision-making in flood-prone areas.

## Flood susceptibility maps are used for
- To forecast flood risk zones.
- To land use planning.
- To reduce flood-related damages.
- To ensure community preparedness.
- To ensure disaster response.

## After Flood hazard, Flood susceptibility maps are used for
- Targeted Relief and Recovery.
- Efficient Infrastructure Repairs.
- Long-Term Resilience Planning.

## Code Explanation

[This code](https://github.com/sefatanam/FloodZoneSnapshot/blob/main/flood_susceptibility_analysis.js) appears to be for working with Google Earth Engine, a platform for analyzing and processing remote sensing data. Here's what it does:

1. It defines a Region of Interest (ROI) based on a filter applied to a table. In this case, it seems to filter data for the location with the 'THANA_NAME' equal to 'Rowmari'.

2. The code sets up an image collection using Copernicus Sentinel-1 Synthetic Aperture Radar (SAR) data, filtering it to select images with specific properties (polarization, instrument mode, and orbit properties).

3. It further filters the collection to select images within specific date ranges ('after' and 'before') and within the defined ROI.

4. The number of images in the 'after' and 'before' collections is printed.
- The code creates mosaic images for 'after' and 'before' collections, selecting the 'VH' (vertical/horizontal) polarization band and clipping them to the ROI.
- The 'before_image' and 'after_image' are added as layers to the map with specified visualization parameters.
- The code exports 'before_image' and 'after_image' to Google Drive as JPG files. It specifies export parameters such as image, description, scale, and region.

5. Finally, it logs a message to indicate that the image export to Google Drive is in progress.

This code is designed to analyze and visualize SAR data for a specific location and date range and export the results for further analysis or use. You can include this explanation in your GitHub readme.md file to help others understand how to use your code and its purpose.