Case Study
Satellite-to-Ground AI Visualization System
Developed a system that transforms satellite imagery into realistic front-facing property visualizations and applies generative style variants at scale.
Challenge
Ohm Outdoor needed a practical workflow to convert address-level satellite imagery into usable, realistic property previews with design variations. The challenge included perspective transformation, geometry inference from limited viewpoint data, and a scalable pipeline for batch operations under real-world data imperfections.
Solution
Helios designed an address-to-visualization pipeline with satellite ingestion, geometry-aware perspective transformation, and a style application engine supporting multiple design themes. Extensive R&D evaluated alternative approaches including 3D APIs, reconstruction models, and multi-angle inference methods. The final architecture prioritized reliability and throughput, delivering production-grade batch processing and predictable outputs suitable for downstream customer-facing applications.
Technical Responsibilities
- Built address-based satellite ingestion and preprocessing workflows
- Designed satellite-to-perspective transformation with geometry inference
- Implemented multi-style generative design transformation pipeline
- Evaluated multiple R&D paths for reconstruction and view synthesis
- Built scalable batch processing and operational monitoring layers
- Optimized for usability, consistency, and real-world data constraints
Outcomes
- Production workflow converting satellite images into practical property-facing visuals
- Scalable style-variant generation for design exploration use cases
- Robust processing under noisy and incomplete real-world imagery inputs
- R&D-backed architecture balancing realism and throughput