Computer Vision

Multi-Class Classification of Urban Regeneration Using a Siamese Network: An Analysis with Real-World Data from Portland, Oregon
Multi-Class Classification of Urban Regeneration Using a Siamese Network: An Analysis with Real-World Data from Portland, Oregon

Expolore the potential of computer vision in urban planning practice

Mar 2, 2025

Multi-Class Classification of Urban Regeneration Using a Siamese Network: An Analysis with Real-World Data from Portland, Oregon
Multi-Class Classification of Urban Regeneration Using a Siamese Network: An Analysis with Real-World Data from Portland, Oregon

This study proposes a deep learning framework using Siamese networks to classify urban regeneration into four categories based on high-resolution aerial imagery. Trained on 2,000 labeled parcels in Portland, Oregon, the model—especially with a U-Net backbone—achieved over 85% accuracy in detecting nuanced changes like redevelopment and demolition. The approach offers a scalable, cost-effective alternative to traditional data sources for monitoring urban regeneration.

Mar 2, 2025

UrbanTrace
UrbanTrace

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.

Oct 1, 2024