  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.
