🎖️ Achievements
- 🏆 2019 Best Young Researcher Paper Award
First International Conference on Smart Technology & Urban Development (STUD)
📄 Abstract
Urban development hinges on improving the Quality of Life (QOL) for city inhabitants. Traditionally, QOL assessments rely heavily on questionnaire surveys, which, while informative, can be costly and time-consuming. Leveraging the rapid advancements in Artificial Intelligence, this work introduces an innovative approach to automatically extract mobility indicators—key components of QOL evaluations—using Semantic Segmentation and Object Recognition techniques. Our method not only enhances the accuracy of transportation mobility assessments but also significantly reduces the data collection costs associated with QOL evaluations.
🌟 Highlights
- AI-Driven Mobility Indicator Extraction: Uses cutting-edge image recognition techniques.
- Efficient Data Gathering: Scalable and cost-effective QOL evaluation.
- Award-Winning Research: Best Young Researcher Paper at STUD 2019.
🖼️ Image Reference
Source: Bangkok Garden
🚀 Getting Started
📥 Installation
git clone https://github.com/kaopanboonyuen/QOL-TransportAI.git
cd QOL-TransportAI
pip install -r requirements.txt⚙️ Configuration
Edit config.yaml to suit your dataset.
📊 Usage
- Preprocessing:
python preprocess.py --data_path /path/to/data --output_path /path/to/output - Training:
python train.py --config config.yaml - Evaluation:
python evaluate.py --model_path /path/to/model --test_data /path/to/test_data - Inference:
python inference.py --image_path /path/to/image.png --output_path /path/to/output.png
🗂️ Project Structure
TransportationMobilityFactorExtraction/
├── data/ # Datasets and preprocessing scripts
├── models/ # Model architectures and training scripts
├── config.yaml # Configuration file
├── train.py # Training script
├── evaluate.py # Evaluation script
├── inference.py # Inference script
└── README.md # Project documentation📌 Citation
@inproceedings{kijsirikul2019transportation,
title={Transportation mobility factor extraction using image recognition techniques},
author={Kijsirikul, Boonserm and Panboonyuen, Teerapong and Iwahori, Yuji and Hayashi, Yoshitsugu and Vateekul, Peerapon and Achariyaviriya, Witsarut},
booktitle={2019 First International Conference on Smart Technology \& Urban Development (STUD)},
pages={1--7},
year={2019},
organization={IEEE}
}🛡 License
This project is licensed under the MIT License. See the LICENSE file for details.
👏 Acknowledgments
This project was made possible by the contributions of our dedicated team and the support of the research community. Special thanks to the STUD 2019 reviewers for their feedback.