flood-mapping

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

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.

The official repo for ISPRS JP&RS 2024 "DAM-Net: Flood detection from SAR imagery using differential attention metric-based vision transformers"

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

Analyzing NYC's Stormwater Flood Map - Extreme Flood Scenario

Seamless Flood Mapping Using Harmonized Landsat and Sentinel-2 Data

A Collection of Flood Hazard Layers for New York City.

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

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.

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.

Desktop resupply planner for disaster response and humanitarian logistics. Compares truck, drone and parachute airdrop delivery on real terrain, OpenStreetMap roads and wind forecasts, with line-of-sight risk, Sentinel-2 flood detection (U-Net, ONNX) and a schedule. Rust engine, Tauri app, CLI.

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

Deep learning-based flood area segmentation using U-Net with ResNet50 encoder on satellite imagery.

Georgia Tech CS 7643 (Deep Learning) Final Project: Flood Mapping Semantic Segmentation using a U-Net Model with Feature Representations of Sentinel-1 and Sentinel-2 Data

PyTorch U-TAE model for binary water and land segmentation from Sentinel-1 and Sentinel-2 imagery using the IBM Granite flood dataset.

Benchmarking CNN and Transformer models for cross-geographic flood segmentation from Sentinel-1 SAR imagery (Sen1Floods11). MSc dissertation, University of Hull.

Sentinel-1 SAR flood-frequency and LiCSBAS InSAR subsidence mapping for the Semarang–Demak coast (2020–2025), fused into a per-kelurahan coastal-flood vulnerability typology. SDG 11.5 / 13.1.

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.

This project uses sentinel 2 satellite data to threshold band 8 NIR into water and non-water pixels, returning total acreas flooded within each flood unit. The generated reports include flooded acreage by unit and a map of flooding. Input parameters: start date and a surface reflectance threshold

Sentinel-1 SAR flood detection for Assam, benchmarked on Sen1Floods11.