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Cyclone Intensity Estimation using Deep Learning

This application provides a user-friendly interface for uploading INSAT-3D IR Satellite Images of cyclones. These images are then processed by a state-of-the-art Deep Convolutional Neural Network (CNN) built using TensorFlow.

This CNN model, featuring multiple convolutional layers and max-pooling operations, has been meticulously trained on a diverse dataset of cyclone imagery. This model is designed to accurately estimate cyclone intensity from raw INSAT-3D satellite images. By leveraging deep learning techniques, our model offers a precise estimation of cyclone intensity directly from satellite imagery, eliminating the need for traditional methods and ensuring accurate results.

The Streamlit app is hosted on Streamlit community cloud.

Streamlit Link: Link

Dataset

Curated Dataset Link: Curated Dataset Link

Google Collab Code

Google drive link: Link.

Screenshots

1 Cyclone Intensity Detector

2 Cyclone Intensity Detector

Key Features:

  1. Image Upload: Users can easily upload INSAT-3D IR Satellite Images of cyclones directly through the intuitive interface.

  2. CNN Processing: The uploaded images are seamlessly passed through our deep CNN model, leveraging cutting-edge TensorFlow technology.

  3. Instant Results: Within moments, users receive real-time estimations of cyclone intensity in knots, providing swift and accurate insights.

Benefits:

  • Accuracy: Our CNN model delivers highly precise estimations of cyclone intensity, surpassing traditional methods.

  • Efficiency: By automating the intensity estimation process, users save time and resources, enabling rapid decision-making.

  • Accessibility: The user-friendly interface makes it accessible to both experts and non-experts in cyclone tracking and analysis.

Get Started: Experience the power of AI-driven cyclone intensity estimation by uploading your INSAT-3D IR Satellite Images now!

Tech Stack Used

Tools : Tensorflow-keras,python,Streamlit

Getting Started

To run the Cyclone Intensity Detector locally, follow these steps:

  1. Clone the repository:

    git clone https://github.com/awanishyadav967/Cyclone-detector.git

  2. Install the necessary dependencies:

    pip install -r requirements.txt

  3. Run the application:

    streamlit run app.py

Feedback

If you have any feedback or suggestions, feel free to open an issue or submit a pull request.

Note

App may went to sleep mode ,click-"Yes,get this app back" (it will take few second to open)