Designing data‑driven tools for fairer urban futures.
I’m Xuanyu Zhou. I trained as a planner at Zhejiang University, spent a semester at UC Berkeley’s College of Environmental Design, and now study at Penn. My research asks who gets the good parts of a city — shade, green space, quick trips, a fast recovery after a crisis — and measures it with remote sensing, simulation, and machine learning. Lately I’m also testing where LLMs are genuinely useful in planning work.
Research
All publications →-
01
Built environment & equity
Green space, heat exposure, and street-level conditions across communities, read from satellite imagery, LiDAR, and street view.
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02
Mobility systems
Simulation-first frameworks for urban air mobility and shared mobility, tested against real metropolitan demand.
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03
Resilience & health
Geospatial machine learning that links urban form to post-pandemic recovery, everyday vitality, and public health.
On my desk
Everything here is a link — hover the laptop for selected projects.
News
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Started the Master of City Planning program at the University of Pennsylvania Weitzman School of Design.
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Submitted a systematic review of 1,446 planning studies to Landscape and Urban Planning as corresponding author.
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Presented PedSVF: Multimodal Aerial–Street Grounding for Pedestrian-Scale Sky View Factor Estimation at Geoinformatics 2026, National University of Singapore. The paper is under review at ACM SIGSPATIAL 2026.
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Graduated from Zhejiang University as an Outstanding Graduate, with an Outstanding Undergraduate Thesis award.