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.
Our CNN model has been meticulously trained on a diverse dataset of cyclone imagery, comprising raw INSAT-3D satellite images, to accurately estimate cyclone intensity. This eliminates the need for traditional methods, ensuring precise center determination and intensity estimation directly from satellite imagery.
Key Features:
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Image Upload: Users can easily upload INSAT-3D IR Satellite Images of cyclones directly through the intuitive interface.
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CNN Processing: The uploaded images are seamlessly passed through our deep CNN model, leveraging cutting-edge TensorFlow technology.
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Instant Results: Within moments, users receive real-time estimations of cyclone intensity in knots, providing swift and accurate insights.
Benefits:
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Accuracy: Our CNN model delivers highly precise estimations of cyclone intensity, surpassing traditional methods.
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Efficiency: By automating the intensity estimation process, users save time and resources, enabling rapid decision-making.
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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!
Streamlit Link: Link
##Jupyter Code
Tools : Tensorflow-keras,python,Streamlit
To run the Cyclone Intensity Detector locally, follow these steps:
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Clone the repository:
git clone https://github.com/awanishyadav967/Cyclone-detector.git
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Install the necessary dependencies:
pip install -r requirements.txt
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Run the application:
streamlit run app.py
If you have any feedback or suggestions, feel free to open an issue or submit a pull request.
App may went to sleep mode ,click-"Yes,get this app back" (it will take few second to open)