A JavaScript code developed in Google Earth Engine (GEE) Platform to Detect Flooded Area along with Affected Population

• UpdatedOct 1, 2019
• JavaScript

This GitHub repository contains the machine learning models described in Edoardo Nemnni, Joseph Bullock, Samir Belabbes, Lars Bromley Fully Convolutional Neural Network for Rapid Flood Segmentation in Synthetic Aperture Radar Imagery.

• UpdatedAug 4, 2023
• Jupyter Notebook

This repository contains a Jupyter Notebook for automatic flood extent mapping using space-based information.

• UpdatedFeb 21, 2023
• Jupyter Notebook

Analyzing NYC's Stormwater Flood Map - Extreme Flood Scenario

• UpdatedNov 14, 2023
• Jupyter Notebook

A Collection of Flood Hazard Layers for New York City.

• UpdatedNov 21, 2023
• Jupyter Notebook

Seamless Flood Mapping Using Harmonized Landsat and Sentinel-2 Data

• UpdatedNov 5, 2024
• Python

This repository includes an automatic statistical-based flood mapping approach for ALOS2 Level 2.1 data. Additionally, this method of flood extraction utilizes Google Earth Engine's open data and processing capabilities.

• UpdatedMar 2, 2023
• Jupyter Notebook

A Collection of NASA ARSET Courses for Flood Mapping and Synthetic Aperture Radar (SAR)

• UpdatedFeb 2, 2024

In this repository, I share a class project in which I explored the Google Earth engine sentinel 1 SAR dataset potential to be used for flood mapping of the 2019 Gorgan flood.

• UpdatedFeb 4, 2025
• Jupyter Notebook

Intelligent Data Solution - Disaster Risk Reduction is a system to assist flood management in the state of Assam through data-driven ways. The repository contains codes to extract relevant datasets and the modelling approach used to calculate Risk Scores for each revenue circle in Assam.

• UpdatedFeb 11, 2025
• Jupyter Notebook

A remote-sensing based application using Google Earth Engine for flood mapping and impact assessment.

• UpdatedFeb 10, 2025
• JavaScript

We are using Sentinel-2 satellite imagery and a specialized U-Net deep learning model to detect changes in landscapes before and after flood events. Using the OMBRIA dataset, the model reliably identifies flooded areas to support disaster management and response efforts.

• UpdatedJan 21, 2025
• Jupyter Notebook

Submission for Kenguruji team for Arnes Hackathon 2024

• UpdatedMay 14, 2024
• Jupyter Notebook

Improving Seamless Flood Mapping with Cloudy Satellite Imagery via Water Occurrence and Terrain Data Fusion

• UpdatedMar 19, 2025