Flood-Detection-System

Real-Time Floodwater Mapping using Satellite Image Segmentation (U-Net)

📌 Project Overview

This project is a Deep Learning application designed to detect and map floodwater from satellite imagery in real-time. It utilizes a U-Net++ architecture with an EfficientNet-B4 encoder to perform semantic segmentation, accurately identifying water bodies and calculating the percentage of flooded areas.

The system features a Flask backend for model inference and a React (Vite) frontend for an interactive user interface.

🚀 Key Features

• AI-Powered Detection: Uses PyTorch and Segmentation Models PyTorch (SMP) for high-accuracy flood masking.
• Real-Time Visualization: Generates an overlay mask showing the exact location of floodwaters.
• Damage Assessment: Automatically calculates the percentage of the area affected by flooding.
• Interactive UI: Modern, responsive interface built with React and Tailwind CSS.

🛠️ Tech Stack

• Frontend: React.js, Vite, Tailwind CSS
• Backend: Python, Flask, Flask-CORS
• Deep Learning: PyTorch, U-Net++, EfficientNet, Albumentations, OpenCV

📂 Project Structure

• /backend: Contains the Flask API, model inference logic (app.py), and the trained model.
• /frontend: Contains the React web application.
• /dataset: (Optional) Scripts for synthetic data generation and training.

🔧 How to Run

1. Backend:

   cd backend
   pip install -r requirements.txt
   python app.py

2. Frontend:

   cd frontend
   npm install
   npm run dev