Occupancy monitoring
Detects available and occupied spaces from camera feeds and structured parking layouts.
Smart parking intelligence for urban operators
We are building a cloud-based platform for campuses, cities, and private operators that combines computer vision, real-time data processing, and analytics to reduce congestion and improve parking decisions.
Product
Parking decisions are often made with incomplete information. Udesa Estaciona turns camera and occupancy data into useful indicators for daily operations, planning, and future policy evaluation.
Detects available and occupied spaces from camera feeds and structured parking layouts.
Tracks usage over time to identify peak periods, underused areas, and recurring bottlenecks.
Supports data-driven actions for routing, pricing experiments, enforcement, and space allocation.
Technology
The platform is being developed as an applied AI product with a modular backend for video ingestion, parking-space geometry, vehicle detection, occupancy estimation, and dashboard-ready analytics.
AWS credits would be used to support compute, storage, APIs, deployment environments, and experimentation needed to move from local prototypes to a reliable cloud-hosted product.
Stage
Udesa Estaciona started from applied AI work at Universidad de San Andres and is being shaped into a product for organizations that manage constrained parking capacity.
Computer vision pipeline for detecting cars and estimating parking space occupancy.
Testing with recorded footage, defined parking geometries, and performance diagnostics.
Next step: scalable data processing, storage, monitoring, and a web dashboard for operators.
Contact
For startup, product, or technical inquiries, contact the founding team through the project email.