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

UrbanTrace

Oct 1, 2024 · 1 min read
Go to Project Site

UrbanTrace is an innovative open-source computer vision toolbox we developed to detect urban changes at the building level using very high-resolution (VHR) aerial imagery.

Last updated on Oct 1, 2024
Computer Vision Geospatial QGIS
Yang Yang, PhD 杨旸
Authors
Yang Yang, PhD 杨旸
Geospatial AI | Computer Vision | Urban Analytics

San Franscisco Flood Health Vulnerability Analysis Oct 4, 2022 →

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