Disaster Management and Mitigation Measures
Project Proposal
Team Members:
Emmanuel Gudinho 9609
Soham Ladgaonkar 9616
Omkar Surve 9643
Ryan D’Mello 9598
Leslie D’silva 9599
Project Title : Wildfire Detection System using Computer Vision
1.Introduction
Wildfires are notoriously unpredictable and known for their rapid spread,
driven by natural factors like temperature, dryness, and wind. Detecting
fires at their incipient stages is crucial, yet it remains a daunting task for
human intervention, particularly when faced with deep forest fires in
remote areas.
The proposed project aims to develop and deploy a Wildfire Detection
System that utilizes image datasets,machine learning algorithms/computer
vision to detect wildfires in real-time, allowing rapid response and
mitigation. This innovative solution will help minimize wildfire damage,
assist in resource allocation, and provide early alerts for high-risk areas.
2.Objectives
Early Detection of Fire and Smoke Using Computer Vision: Implement
advanced computer vision techniques to detect fire and smoke with high
accuracy and efficiency. By using YOLOv8, the system will offer rapid
analysis of visual data, enabling earlier detection than traditional methods.
Object Detection with YOLOv8: Leverage YOLOv8's robust object
detection capabilities to identify fire and smoke in images and videos
swiftly. By analyzing and comparing different model sizes of YOLOv8,
the project aims to find the optimal balance between detection speed and
accuracy to meet diverse operational needs.
Enhanced Model Accuracy with Specialized Fire Dataset: Improve
model accuracy for wildfire detection through transfer learning. A
pre-trained YOLOv8 model on the COCO dataset will be fine-tuned with
the D-Fire dataset, which includes annotated images of fire and smoke,
making the model robust against false positives such as clouds and other
look-alike elements.
Efficient Data Processing and Model Training: Employ data
preprocessing techniques to prepare the D-Fire dataset, ensuring
high-quality input for training the YOLOv8 model. This objective focuses
on optimizing the dataset to reduce noise, enhance relevant features, and
facilitate effective training of the wildfire detection model.
3.Components of the System
TensorFlow and Keras
TensorFlow, with its high-level API Keras, is utilized to create and
train deep learning models for image recognition tasks.In the context
of wildfire detection, these libraries are used to train a Convolutional
Neural Network (CNN) model to analyze satellite images for
identifying wildfire indicators (like smoke or heat spots).
TensorFlow enables the efficient handling of large image datasets
and facilitates model deployment and tuning.
OpenCV
OpenCV (Open Source Computer Vision Library) is a powerful tool
for image processing and computer vision tasks.OpenCV processes
satellite images, applying techniques such as resizing, filtering, and
segmenting images to enhance features relevant to wildfire
detection. These preprocessing steps are essential for preparing raw
satellite images for input into the CNN model.
Flask
Flask is a lightweight web framework for building RESTful APIs
and web applications.Flask is used to create an API that serves the
wildfire detection model, allowing users to make requests with
satellite images and receive a classification response (whether
wildfire indicators are present). This API structure makes it possible
to integrate the model with other applications or services that need
real-time wildfire detection information.
4. NumPy and Pandas
NumPy and Pandas are foundational libraries in Python for handling
numerical data and large datasets.These libraries support data
manipulation and analysis, helping in loading, preprocessing, and
transforming the satellite image datasets. They also play a role in
managing and structuring data used to train and validate the model.
Matplotlib and Seaborn
Matplotlib and Seaborn are visualization libraries used for data
analysis and interpretation.In this project, these libraries help
visualize the dataset distribution, model training progress, and
performance metrics such as accuracy and loss. This visualization is
critical for evaluating the effectiveness of the model and identifying
potential improvements.
Machine Learning Pipeline and Data Augmentation
The repository includes data augmentation techniques and a machine
learning pipeline to handle image data efficiently.Data augmentation
(e.g., rotating, flipping, and cropping images) helps increase the
dataset’s size and diversity, making the model more robust. The
pipeline then manages each step in model training, from data loading
to preprocessing, augmentation, and feeding the images into the
CNN.
4. Multiple Uses of the Wildfire Detection System
4.1 Early Wildfire Detection
Remote Monitoring: The system will detect heat signatures and
potential wildfire sources in forests and remote areas, providing
immediate alerts to local authorities.
Community Alerts: Through the mobile app, residents in high-risk
areas can receive alerts and warnings to prepare or evacuate as
needed.
4.2 Risk Assessment for Emergency Services
Resource Optimization: By identifying high-risk zones, the system
will help emergency services allocate resources effectively, reducing
response times and improving containment.
Preemptive Warnings: Using risk assessments based on weather and
vegetation data, authorities can be alerted to heightened wildfire risk,
enabling preventive measures.
4.3 Environmental Management and Planning
Habitat Protection: The system will help protect wildlife habitats by
providing data that can inform controlled burns and land
management decisions.
Climate Change Mitigation: The data collected can be used to
understand wildfire trends in relation to climate change, assisting
researchers and policymakers in making informed decisions.
5. Implementation Strategy
5.1 Phase 1: Research and Prototype Development
Research suitable data sources, including satellite imagery and
weather data, and test machine learning models on historical wildfire
data.
Develop a prototype using open-source datasets and deploy it in a
controlled environment for initial testing and refinement.
5.2 Phase 2: Installation
Implement the system in a wildfire-prone region, focusing on
real-time data collection, risk assessment, and wildfire detection
capabilities.
Collect feedback from local emergency services and community
members to improve system usability and accuracy.
5.3 Phase 3: Deployment
Expand the deployment to multiple high-risk regions, collaborating
with government agencies and environmental organizations.
Launch a public awareness campaign to educate communities about
wildfire safety and encourage app usage.
6. Expected Outcomes
● Reduced Wildfire Damage
● Optimized Resource Allocation
● Enhanced Public Safety
7. Conclusion
The Wildfire Detection System addresses the urgent need for a reliable and
efficient approach to wildfire detection and response. By integrating
advanced data analytics, satellite technology, and machine learning, this
project aims to reduce the impact of wildfires on communities and
ecosystems. The system will serve as a critical tool for government
agencies, firefighters, and local residents in safeguarding lives and the
environment.
With successful implementation, this project could be scaled to
wildfire-prone areas globally, helping mitigate one of the most destructive
natural disasters of our time.
