“We quantify streets, squares, and parks.”



UDE (Urban Data Eye) is a firm that diagnoses the health of public spaces
to address issues related to their malfunctioning, safety, congestion, or accidents.

Panel 1


UDE addresses the following issues:

  • Lack of precise, rigorous, and continuous information about pedestrians’ behavior at intersection, streets, and squares.
  • Errors in the data self-reported or provided by survey takers on site which add noise to the analysis.
  • Difficulty to perform studies in non-conventional locations.

UDE proposes the following solutions:

  • Using an objective, continuous, and scientific observer that can watch the whole public space: CCTV and live streaming cams.
  • Implementation of an algorithm to the images for extracting all the information necessary to detect the problems affecting public space.
  • Obtaining a study with an accuracy of 95% on pedestrian-based mobility that can measure anything visible.
Panel 2



“We visualize paths and speed of urban objects”

To rise exponentially the precision of data for decision making in cities. UDE data are objective, geolocated, and exact.

Real-time results from the city and its evolution. The information gathered with UDE can be constantly updated and renewed without limits.

Global understanding of the public realm through an objective observer (bot). UDE cams are a tireless reporter, 24/7 active and precise.

Classification of data based on the mode of transportation. UDE algorithm allows classifying agents automatically (pedestrians, bikes or cars).

Understandable communication of information to users. The maps produced by UDE are easy to read for any user.

Panel 3


Rodrigo Delso Gutiérrez (Architect, IIT in Chicago and UCL in London) specialized in critical urban theory and digital media applied to cities.

Javier Argota Sánchez-Vaquerizo (Architect, IIT in Chicago and Carnegie Mellon in Pittsburgh) specialized in big data, digital cartography, and new technologies applied to design and urban planning.

Iago Romero Ogando (Architecture/Computer Science) specialized in programming architecture through Artificial Intelligence and Machine Learning.