This project focuses on developing a machine learning model to accurately detect and assess areas damaged by flooding. Utilizing advanced image processing and deep learning techniques, this model aims to provide rapid and reliable damage assessments, which are crucial for effective disaster response and recovery efforts.
Damage Detection: Utilizes state-of-the-art convolutional neural networks (CNNs) to analyze aerial or satellite imagery and identify flood-affected areas. Damage Assessment: Classifies the severity of damage to aid in prioritizing response efforts. Real-time Analysis: Designed to deliver quick results, enabling faster decision-making in critical situations. High Accuracy: Trained on a diverse dataset to ensure reliable performance across different geographical locations.
Python 3.x TensorFlow/Keras OpenCV NumPy, Pandas (Other libraries/tools used) Getting Started Prerequisites Python 3.x Pip package manager
Clone the repository: bash Copy code git clone https://github.com/uchiharon/AI4Good-Flood_Detection_Using_Deep_Learning.git Navigate to the project directory: bash Copy code cd AI4Good-Flood_Detection_Using_Deep_Learning Install the required packages: Copy code pip install -r requirements.txt Usage Explain how to run the model, including any scripts and command-line arguments. Provide examples if necessary.
Describe the dataset used for training and evaluating your model. Include sources, and how it was preprocessed, if applicable.
Offer an overview of the model architecture, training process, and performance metrics.
Contributions to this project are welcome. Please follow these steps to contribute:
Create a new branch (git checkout -b feature-branch). Make your changes and commit them (git commit -am 'Add some feature'). Push to the branch (git push origin feature-branch). Create a new Pull Request.