Machine Learning Computer Vision Applications for Spatial AI Object Recognition in Orange County, California

TL;DR

This paper presents an integrated AI and computer vision system for spatial object recognition in Orange County, California, utilizing multi-sensor data, deep neural networks, and panoramic imagery to automate asset detection and support real-time spatial data applications.

Contribution

The paper introduces a comprehensive methodology combining multi-sensor data, deep learning, and panoramic imagery for automated spatial object recognition in a real-world setting.

Findings

Successfully processed over 800,000 images for object detection.

Achieved effective recognition of stop-signs and fire hydrants.

Demonstrated potential for real-time spatial data automation.

Abstract

We provide an integrated and systematic automation approach to spatial object recognition and positional detection using AI machine learning and computer vision algorithms for Orange County, California. We describe a comprehensive methodology for multi-sensor, high-resolution field data acquisition, along with post-field processing and pre-analysis processing tasks. We developed a series of algorithmic formulations and workflows that integrate convolutional deep neural network learning with detected object positioning estimation in 360{\deg} equirectancular photosphere imagery. We provide examples of application processing more than 800 thousand cardinal directions in photosphere images across two areas in Orange County, and present detection results for stop-sign and fire hydrant object recognition. We discuss the efficiency and effectiveness of our approach, along with broader inferences related to the performance and implications of this approach for future technological innovations, including automation of spatial data and public asset inventories, and near real-time AI field data systems.