How OceanIR AI Works
OceanIR uses a multi-layered AI system that analyzes images in approximately 850 milliseconds. Each layer contributes specialized intelligence to achieve precise geolocation results.
Vision Analysis
STEP 1The AI examines visual features in your image: architectural styles, building materials, street layouts, vegetation types, signage, and environmental context. This creates a detailed visual fingerprint of the location.
Text & Symbol Detection
STEP 2OceanIR reads and interprets text visible in the image: street signs, business names, building numbers, license plates (when visible), and directional markers. This textual data provides critical location clues.
Geographic Intelligence
STEP 3The AI combines visual and textual clues with its trained knowledge of the selected city. It references thousands of known locations, architectural patterns, and geographic features specific to Miami, New York, Los Angeles, or Chicago.
Precision Mapping
STEP 4OceanIR generates precise GPS coordinates and validates them against MapBox's geocoding service. The system provides confidence scores and visual verification through street view imagery, ensuring accuracy.
Lightning-Fast Analysis
Despite the complexity of the process, OceanIR completes full analysis in approximately 850 milliseconds (less than one second). This speed is achieved through:
Optimized AI models: City-specific training reduces processing overhead and improves accuracy.
Parallel processing: Multiple analysis layers run simultaneously rather than sequentially.
Efficient architecture: Motia API proxy handles request optimization and caching.
What Makes OceanIR Accurate
City-specific training: Each model is trained exclusively on images from its target city, learning unique architectural styles, street patterns, and geographic features.
Multi-source validation: Results are cross-referenced with MapBox geocoding, street view imagery, and local landmark databases for verification.
Continuous learning: The AI improves over time as it analyzes more images and receives feedback on accuracy.
Privacy-first design: All analysis happens in real-time without storing sensitive image data, maintaining both speed and security.