Team Leader
Yuna Yoon
Texas A&M University
Department of Landscape Architecture and Urban Planning
hw.yoon@tamu.edu
Project Type
Research
Who Can Join
Masters Students, Undergraduate Students
Project Description
Have you ever wondered if a computer could “see” the world like we do? Computer vision is a branch of artificial intelligence that teaches machines to recognize patterns and objects in images. It’s the same technology behind things like face unlock on phones, self-driving cars, and even social media filters.
In this project, we use computer vision to scan thousands of street-level images and automatically spot signs of neighborhood conditions. For example, the system can detect garbage on the sidewalk, graffiti on a wall, a boarded-up building, or a vacant lot. Instead of sending survey teams to walk every block, computer vision can process huge amounts of visual data quickly and consistently.
The fun part is that the computer doesn’t just see one thing—it can be trained to identify multiple features at once. By labeling examples of sidewalks, buildings, or litter, we “teach” the AI model what to look for. Once trained, it can then find the same things in entirely new places.
Why does this matter? Because the physical environment around us is closely linked to how people feel and how healthy they are. By connecting computer vision results with health data, we can explore how the look of a neighborhood influences well-being.
Team Needs
Students with coding experience – especially those who have worked on projects using Python, machine learning, or data analysis.
Students interested in computer vision – anyone curious about how AI can “see” and analyze images.
People who enjoy labeling and organizing data – helping to build the training datasets that teach the computer what to recognize.
Students curious about health and society – connecting what the computer sees in neighborhoods to real impacts on people’s well-being.
Creative thinkers and communicators – to help share results in a way that’s engaging for both technical and non-technical audiences.
Special Opportunities
This project offers students a chance to work hands-on with cutting-edge AI tools and see how computer vision can be applied to real-world problems. By joining the team, students will:
Build practical coding and AI skills that are in high demand across industries like tech, data science, and urban planning.
Gain experience with computer vision models (such as YOLO and DeepLab) and learn how to train them to detect meaningful features in images.
Work with real-world datasets (like Google Street View images and health-related data), bridging the gap between technology and social impact.
Collaborate in a multidisciplinary team, combining computer science, urban studies, and public health perspectives.
Strengthen research and presentation experience, with opportunities to contribute to conferences, posters, or publications.