Flood Detection Using Deep Learning Details
Flood Detection Using Deep Learning Details
Detects whether an area is flooded or not using satellite images
Project Description
This project focuses on building a web-based system to detect flood-affected areas from images using deep learning. A Convolutional Neural Network (CNN) based on the VGG16 architecture is trained to analyze satellite or aerial images and identify regions impacted by flooding. The trained model is deployed using Python Flask, and the frontend interacts with the backend through REST APIs, allowing users to upload images and receive real-time predictions via a web interface.
Features
Detection of flood-affected areas from images
VGG16-based CNN model for image classification
Image upload and prediction through a web interface
Backend deployment using Flask REST APIs
Visualization of prediction results
Suitable for final-year projects and disaster management studies
Tech Stack
Python
TensorFlow (Deep Learning)
Convolutional Neural Networks (CNN)
VGG16 Architecture
Flask (Backend Framework)
HTML, CSS, JavaScript (Frontend)
NumPy and other supporting Python libraries
System Requirements
Python 3.10+
Anaconda
No GPU is required
Deliverables
Jupyter Notebook
Dataset
Setup over video call
WhatsApp support
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