Yang Yang
  • Bio
  • Papers
  • Talks
  • Experience
  • Projects
  • Teaching
  • Publications
    • Evaluate Spatial Equity in New York City’s Vibrant Street Programs with Local Lightweight Vision LLM (Working Paper)
    • Multi-Class Classification of Urban Regeneration Using a Siamese Network: An Analysis with Real-World Data from Portland, Oreg
    • Multi-Class Classification of Urban Regeneration Using a Siamese Network: An Analysis with Real-World Data from Portland, Oregon
    • Identifying Building-Level Urban Changes with Computer Vision
    • Revitalizing Cities: The 5R Framework Approach to Urban Retrofitting and Big Data Insights
    • Assessing New Neighborhood Diversity in Charlotte via Computer Vision
    • LLMs Outperform Traditional NLP in Social Media Analysis for Urban Planning
    • The voices behind congestion pricing: unveiling the social backgrounds of supporters and opponents through a large language model
    • Study on the safety of outdoor activity space for early school-age children in Nanjing - Exploring strategies for optimizing residential neighborhoods and pre-school waiting spaces.
    • An example journal article
  • Recent & Upcoming Talks
    • Multi-Class Classification of Urban Regeneration Using a Siamese Network: An Analysis with Real-World Data from Portland, Oregon
  • Projects
    • UrbanTrace
    • San Franscisco Flood Health Vulnerability Analysis
    • Intro 144B (Uber) Study
  • Projects
  • Experience
  • Teaching
    • Advanced GIS for Urban Planners
  • Blog
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    • index

San Franscisco Flood Health Vulnerability Analysis

Oct 4, 2022 · 1 min read
Go to Project Site

An award-winning project that uses machine learning to identify key influencers of flood risk in San Francisco.

Last updated on Oct 4, 2022
Machine Learning
Yang Yang, PhD 杨旸
Authors
Yang Yang, PhD 杨旸
Geospatial AI | Computer Vision | Urban Analytics

← UrbanTrace Oct 1, 2024
Intro 144B (Uber) Study Sep 26, 2018 →

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