This repository contains the codebase and documentation for a disaster relief and response solution aimed at leveraging advanced technology to enhance disaster management efforts, particularly during events like floods and wildfires. The solution integrates satellite imagery analysis, drone-based victim detection, and AI-driven algorithms to improve situational awareness, resource allocation, and response coordination.
The solution approach can be summarized as follows:
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Detect WildFires in Early stages from Satellite Images The solution utilizes satellite imagery, particularly NOAA-20 satellites called VIIRS, for detection of Wildfires in early stages so that Disaster Relief teams can be sent the information before, expeding Disaster Relief.
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Drone-Based Victim Detection in Floods: Drones equipped with high-resolution cameras are deployed for aerial imagery capture and victim detection in disaster-affected areas. Computer vision algorithms like YOLOv8 analyze drone imagery to identify and localize individuals in need of assistance, facilitating swift search and rescue operations.
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Flood Detection and segmentation from Satellite Imagery AI techniques are applied for data analysis and decision support, including semantic segmentation of satellite imagery,and optimization of resource allocation. These algorithms provide actionable insights to emergency responders and relief agencies, aiding in strategic planning and response coordination.
results in detectFloodVictims/runs/detect/train
To use the disaster relief and response solution, follow these steps:
Use deployed app to check Flood Victim Detection model: https://sahayta.streamlit.app/
UI:
Results:
- Clone the repository to your local machine:
git clone https://github.com/your-username/disaster-relief-solution.git - Install the necessary dependencies and libraries as specified in the documentation.
- Install requirements for streamlit app
pip install -r requirements.txt - Set up the environment and configure the solution parameters according to your requirements.
- Get Your ROBOFLOW_API_KEY from https://universe.roboflow.com/
- To Check results for Victim Detection in Floods
streamlit run app.py - To check Results for Flood Detection and Segmentation and Wildfire detection, Run the provided scripts and modules to execute the solution components, analyze data, and generate insights.
- Output images from Flood Segmentation model training is saved in
./Flood_mapping
This project is licensed under the MIT License.
We would like to acknowledge the contributions of the open-source community and the support of our partners and collaborators in developing and testing this disaster relief and response solution. Thank you for your support!
Note: This README file provides an overview of the disaster relief and response solution and serves as a starting point for understanding the project structure and functionality. For detailed instructions and documentation, please refer to the files in the docs/ directory.