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We provide an integrated and systematic automation approach to spatial objectrecognition and positional detection using AI machine learning and computervision algorithms for Orange County, California. We describe a comprehensivemethodology for multi-sensor, high-resolution field data acquisition, alongwith post-field processing and pre-analysis processing tasks. We developed aseries of algorithmic formulations and workflows that integrate convolutionaldeep neural network learning with detected object positioning estimation in360{\deg} equirectancular photosphere imagery. We provide examples ofapplication processing more than 800 thousand cardinal directions inphotosphere images across two areas in Orange County, and present detectionresults for stop-sign and fire hydrant object recognition. We discuss theefficiency and effectiveness of our approach, along with broader inferencesrelated to the performance and implications of this approach for futuretechnological innovations, including automation of spatial data and publicasset inventories, and near real-time AI field data systems.

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