Lois Anne Leal and Chaitree Sham Baradkar participated in the innovative iRAP Omdena 8 week Challenge earlier this year to find AI-based solutions to map crash risk and generate iRAP star rating attribute data. They were just 2 of 50 AI and machine learning engineers crowd-sourced from around the world focussed on finding a solution for the accelerated and intelligent collection and coding of road attribute data (AiRAP) to save lives.
Lois is a Science Research Specialist, Machine Learning Engineer and Certified TensorFlow Developer from the Philippines and Chaitree is a Data Scientist at PharmaACE and Machine Learning Engineer at Omdena from Pune, India.
The iRAP Omdena Challenge had 3 main objectives:
- Collecter des données géolocalisées sur les accidents et produire des cartes de risque iRAP présentant l'historique des accidents par kilomètre et le nombre d'accidents par kilomètre parcouru pour chaque usager de la route
- Collecte de données sur les caractéristiques des routes, le flux de trafic et la vitesse selon la norme mondiale iRAP et cartographie des performances de sécurité et du classement par étoiles de plus de 100 millions de km de routes dans le monde.
- Élaborer des indicateurs clés de performance reproductibles pour les infrastructures routières, pouvant servir de base au suivi annuel des performances.
Lois and Chaitree contributed to the second objective by automatically sourcing the crucial component of vehicle count under the traffic or vehicle flow attribute using satellite imageries with the help of Artificial Intelligence.
Read their published blog: “Using Convolutional Neural Networks To Improve Road Safety And Save Lives“".
Our thanks to Lois and Chaitree, and all the Challenge volunteers for their valuable contribution!