Extracting urban features from street level images

• Grant number: 20/15990-0
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: January 01, 2021
• End date: December 31, 2021
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Agreement: CNPq - INCTs
• Principal Investigator: Roberto Hirata Junior
• Grantee: Ayrton Amaral Alves Vitor
• Host Institution: Instituto de Matemática e Estatística (IME). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Associated research grant: 14/50937-1 - INCT 2014: on the Internet of the Future, AP.TEM

Abstract

Street Level Images are easy to capture but difficult to analyse because the environment is not structured. Google Street View is the fastest and cheapest way to get street level images to assess urban features in most of the cities in the world. Using INACITY, an Internet framework to gather urban images from Google Street View, we plan to extract urban features from street level images and study the Social Ecological System of some neighborhoods in some cities.