Posted on

Forest wildfire detection from satellite images using Deep Learning

Buy Now ₹1501

In this project, we detect forest wildfire from given satellite images.I have used CNN, with a training dataset of 2000 images.

Model training

Refer to the research.ipynb jupyter notebook to know the steps taken for model development and algorithm.

Technology Used:-

  • We have developed this project using the below technology
  • HTML : Page layout has been designed in HTML
  • CSS : CSS has been used for all the desigining part
  • JavaScript : All the validation task and animations has been developed by JavaScript
  • Python : All the business logic has been implemented in Python

Supported Operating System

  • We can configure this project on following operating system.
  • Windows : This project can easily be configured on windows operating system. For running this project on Windows system, you will have to install
  • Python 3.7, PIP, Flask.
  • Linux : We can run this project also on all versions of Linux operating systemMac : We can also easily configured this project on Mac operating system.

Installing and running this app:

  1. Requirements: Use pip install to download following packages
  2. Tensorflow and Keras
  3. Flask
  4. WSGI server (see the error message when running Flask app, and install all specified packages)
  5. running the app:
  6. run command: python app.py in the project folder
  7. Once the server starts, open browser, the app runs on http://127.0.0.1:5000/
  8. "test satellite images" folder contains some satellite images that you can upload to check the working of this application.

Read Before Purchase  :

  1. One Time Free Installation Support.
  2. Terms and Conditions on this page: https://projectworlds/terms
  3. We offer Paid Customization installation Support
  4.  If you have any questions please contact  Support Section
  5. Please note that any digital products presented on the website do not contain malicious code, viruses or advertising. You buy the original files from the developers. We do not sell any products downloaded from other sites.
  6. You can download the product after the purchase by a direct link on this page.