Effortlessly identify, manage, track, and update asset information within ATLAS, ensuring a seamless 'golden thread' of accurate, up-to-date data. At the core of ATLAS's powerful visualisations is an asset information management layer—designed to unify and track assets across multiple federated data sources. This intelligent framework ensures seamless data integration and provides complete visibility for efficient asset management.
Assets can be created from scratch, automatically generated from 3D models, or imported from third party systems. ATLAS supports ISO 19650 compliant change management workflows, to ensure all your data is auditable, with published records being immutable and trustworthy. Further in-built capability enables advanced AI processing to detect and classify objects within 3D models, point clouds, and videos. It also supports model tagging, design validation and a range of unique processing opportunities, unlocking deeper insights and enhanced automation.
Seamlessly track, capture, verify, and hand over asset data across construction projects, maintenance schemes, and operational workflows. With ATLAS, ensure accuracy and efficiency at every stage by managing asset templates and schemas, locations, relationships and attributes. For on-the-go asset management, our iOS mobile app enables teams to capture and verify data directly from the field, keeping project information up-to-date and easily accessible.
With ATLAS's open API and bi-directional syncing, seamlessly configure assets and manage attribute information within ATLAS while ensuring accurate write-back to native BIM files. Leverage automated model tagging with unique asset identifiers to establish a 'golden thread' of information, enhancing project efficiency and streamlining data management throughout the asset lifecycle.
ATLAS integrates a sophisticated suite of AI models designed for asset recognition, segmentation, and geolocation across federated data sources. By leveraging the unique strengths of each dataset, ATLAS prioritises the most accurate information from each source. For example, in the railway, ATLAS will utilise highly accurate positional data from point clouds while utilising video-based Optical Character Recognition (OCR) to extract key details such as Signal IDs and speed sign content, relating these back to known IDs in the asset database and 3D models.
This intelligent processing enables precise asset association, facilitating inventory building, basic positioning, and advanced applications like vegetation management, signal sighting, and structural gauging.
We have AI model for various industries. For example, take a look at what we have implemented for Rail:
ATLAS for Rail
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