Developing Scalable CNN for Building Damage Identification

Data and Time:

March 28th (Thursday) 12 - 2 PM Central, USA.

Watch course:

Course notes:

Please view the course notes online


This webinar will guide you through building and deploying effective Convolutional Neural Networks (CNNs) for automated building damage identification. We’ll cover image classification techniques, CNN fundamentals, and hands-on experience to empower you with the skills to scale your solutions.

Key Topics:


Basic understanding of Python programming is recommended but not essential.

Trainer Bio:

Sikan Li is a research associate at the Texas Advanced Computing Center (TACC)’s Scalable Computational Intelligence (SCI) group. Her work focuses on developing machine learning and data mining techniques to analyze large-scale, complex datasets. She’s published several papers in this field and actively contributes to research, development, and support initiatives involving big data, statistical analysis, and machine learning at TACC. With a background in scientific data visualization, Sikan brings a unique perspective to her passion for scalable data analysis and machine learning.

Sikan Li