Self-Supervised Urban Analysis

This project uses Self-Supervised Learning (SSL) through DINO, a vision transformer model, to extract visual representations of urban environments from high-resolution satellite imagery. Unlike supervised approaches, SSL learns meaningful visual features without requiring predefined categories or annotated training data. The model captures spatial patterns, textures, building arrangements, and other visual characteristics of the built environment. These representations can then be analyzed to identify recurring urban patterns and reveal morphological similarities and differences across urban areas. By leveraging self-supervised learning, the project provides a scalable, data-driven approach to understanding urban form directly from satellite imagery.