Verizon Patent and Licensing Inc.

Lindy Williams

Kevin Brackney

Joshua D Eno

Bergen Davell

Wesley Boyer

Khoa Truong Pham

John Patrick O'Dwyer

Zeehasham Rasheed

Tristan Storz

An exemplary geospatial image processing system generates, based on multiple detections of an object of interest detected by a computer vision model in multiple, correlated images of a geospatial location captured from different camera viewpoints, user interface content that includes a visual indication of the detected object of interest superimposed at an object position on a view of the geospatial location. The system provides the user interface content for display in a graphical user interface view of a user interface and provides, by way of the user interface, a user interface tool configured to facilitate user validation of one or more of the multiple detections of the object of interest. The system may receive, a user validation of one or more of the multiple detections of the object of interest and may train the computer vision model based on the user validation. Corresponding methods and systems are also disclosed.

Systems and methods for training and validating a computer vision model for geospatial imagery

Exemplary systems and methods perform post-processing operations on computer vision model detections of objects of interest in geospatial imagery to detect and assign attributes to the detected objects of interest. For example, an exemplary post-processing system correlates multiple detections, made by a computer vision model, of an object of interest depicted within a set of images of a geospatial location, determines, based on the correlated detections, an attribute of the object of interest depicted within the set of images of the geospatial location, and selects the attribute for inclusion in a dataset for the object of interest. Corresponding methods and systems are also disclosed.

Methods and systems for detecting and assigning attributes to objects of interest in geospatial imagery

A device can receive a data model that has been trained on base map data and summary statistics data associated with a first geographic region. The device can obtain additional base map data associated with a second geographic region and additional summary statistics data for a set of junctions within the second geographic region. The device can determine traffic control mechanisms associated with the set of junctions by providing the additional base map data and the additional summary statistics data as input for the data model. The device can generate, using output of the data model, a base map that includes information indicating whether the set of junctions include traffic control mechanisms. The device can, after generating the base map, perform one or more actions associated with improving vehicle navigation or traffic management.

Identification of traffic control mechanisms using machine learning

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Broomfield, CO, United States of America

Lindy Williams