Building Building Inspection Intelligence with AWS Spatial Data

Inspection teams in industrial settings generate large volumes of images and observation data, but struggle with data reuse due to metadata inconsistency and loss of context. Addressing this requires an approach that treats inspection outputs not as simple files but as spatially referenced data.

AWS Spatial Data Management (SDMA) provides an architectural pattern for managing spatially referenced data throughout the lifecycle of physical assets. SDMA collects on-site image and sensor data, links it to spatial identifiers, and maintains governance in a centralized repository.

The proposed solution architecture consists of the following layers:

• User and API layer: Supports various interfaces through the Spatial Data Portal and REST API
• Integration layer (SDMA compute): Handles request processing and business logic
• AI inference workflow: Uses Amazon SageMaker to automatically detect defects in images and return structured results
• Data plane: Uses Amazon S3 to maintain a single source of truth for spatially indexed content
• Control plane: Manages workflows and metadata through serverless services such as AWS Lambda

This structure gives data clear spatial context, preserving data integrity over time and enabling its use as a high-quality dataset for training machine learning models.