Developing a Forest Fire Detection and Monitoring System for Algeria using Satellite Imagery and Machine Learning

Local Chapter Algeria, Local Chapter

Coordinated byAlgeria ,

Rabee Gheni

Status: Completed

Project Duration: 15 Apr 2023 - 15 Jun 2023

Open Source resources available from this project

Github Repository

Project background.

Algeria has been facing a high risk of forest fires due to several factors such as the dry and hot climate, extensive forest areas, and human activities. These forest fires have a significant impact on the environment, biodiversity, and economy of the country. Therefore, there is a pressing need for an effective forest fire detection and monitoring system in Algeria.

Satellite imagery and machine learning can be used to develop a system that can detect and monitor forest fires in real time. By analyzing the satellite imagery of forest areas, the system can identify changes in temperature, humidity, and vegetation that could indicate a potential forest fire. Machine learning algorithms can be trained to detect and classify these changes, and alert the authorities in real time, allowing them to take quick action to prevent the spread of fires and reduce the damage caused.

The project aims to develop a Forest Fire Detection and Monitoring System for Algeria using Satellite Imagery and Machine Learning. The system will be designed to work in real time and provide alerts to the relevant authorities in the event of a forest fire. The project will leverage the latest advancements in machine learning and image processing to achieve high accuracy and reliability.

The successful implementation of the Forest Fire Detection and Monitoring System can have a significant impact on reducing the damage caused by forest fires in Algeria. It can also serve as a blueprint for other countries facing similar challenges, and contribute to global efforts to mitigate the impact of climate change on our environment.

The problem.

Forest fires in Algeria have been a major problem, causing significant ecological and economic damage. Despite efforts to prevent and combat these fires, they continue to occur, often resulting in the loss of lives, homes, and natural resources. Traditional methods of monitoring and detecting fires, such as ground patrols and surveillance towers, have proven to be inadequate in terms of efficiency and accuracy.

To address this issue, there is a need for a more effective and efficient system for detecting and monitoring forest fires in Algeria. This project aims to develop a Forest Fire Detection and Monitoring System using satellite imagery and machine learning to help prevent and control forest fires in Algeria.

Project goals.

In this project, the Omdena team aims to develop a Forest Fire Detection and Monitoring System for Algeria using Satellite Imagery and Machine Learning. The project's primary goal is to accurately detect and monitor forest fires in real-time, enabling rapid response and mitigation efforts. With a duration of 8-weeks, this project aims to:1. Data Collection: Collect and preprocess satellite imagery data for Algeria. 2. Exploratory Data Analysis: Analyze and visualize the collected data to gain insights. 3. Model Development: Develop a Deep Learning model using Convolutional Neural Networks (CNN) to detect forest fires in satellite imagery. 4. Training and Optimization: Train and optimize the developed model using various techniques such as transfer learning and data augmentation. 5. Model Evaluation: Evaluate the model's performance on a test dataset and fine-tune the model as needed. 6. Real-time Monitoring: Develop a web-based app to monitor and visualize the detected forest fires in real-time. 7. Deployment: Deploy the developed system for use by local authorities and stakeholders in Algeria.Overall, the Forest Fire Detection and Monitoring System will provide valuable insights and early warnings to mitigate the devastating effects of forest fires on the environment and human lives

Project plan.

  • Week 1

    1. Research previous work and identify suitable data sources
    2. Set up project repository and data management system

  • Week 2

    1. Data Collection from satellite imagery and preprocessing
    2. Exploratory Data Analysis to understand the characteristics of the data

  • Week 3

    1. Feature Extraction and selection
    2. Develop and fine-tune Machine Learning models for Forest Fire detection

  • Week 4

    1.Validate and optimize Machine Learning models for Forest Fire detection 2.Create visualizations and presentations for the results of the model

  • Week 5

    1. Deploy and test the developed Machine Learning models on a test set
    2. Analyze and compare the results of different models for Forest Fire detection

  • Week 6

    1. Integrate the developed models into an App or dashboard to display Forest Fire detection

  • Week 7

    1. Further testing of the App and refine the system for efficient Forest Fire detection. 2. Documenting the process and report writing

  • Week 8

    1. Final testing and evaluation of the developed system. 2. Prepare the final project report and submit to Omdena.

Learning outcomes.

Satellite imagery analysis, Forest fire detection and monitoring systems, Machine learning and deep learning techniques for image recognition, Geospatial data analysis, Project management and collaboration with a global team

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