Geolocation & Spatial Features
Relevant source files
The following files were used as context for generating this wiki page:
- TAMV-MASTER-DOCUMENTATION.md
- ARCHIVE/tamv-docs-consolidated-2026-02-02/TAMV-OFFICIAL-SYSTEM-DESCRIPTION.md
- ARCHIVE/remaining-docs-consolidated-2026-02-13/TAMV-COMPLETE-IMPLEMENTATION-PLAN.md
- ARCHIVE/remaining-docs-consolidated-2026-02-13/REPORTE-AVANCE-TECNICO-COMPLETO.md
- scripts/analyze-duplicates.ts
- ARCHIVE/remaining-docs-consolidated-2026-02-13/TAMV-BACKEND-COMPLETE.md
Geolocation and spatial features in the TAMV ecosystem provide the foundation for integrating physical location data with immersive virtual environments. These features support the "DreamSpaces" engine, enabling virtual worlds to interact with real-world spatial coordinates and quantum physics simulations. The system utilizes advanced AI, specifically the Isabella framework's "Spatial AI" application, to process 3D environments and provide intelligent navigation and object recognition.
The architecture bridges the gap between traditional social networking and Web 4.0/5.0 immersive experiences. By leveraging a production-ready stack that includes PostgreSQL with pgvector for spatial embeddings and MinkowskiEngine for sparse 3D processing, TAMV creates a persistent, verifiable link between users, assets, and their geographic or virtual locations.
Sources: TAMV-MASTER-DOCUMENTATION.md:108-115, TAMV-OFFICIAL-SYSTEM-DESCRIPTION.md:27-35, REPORTE-AVANCE-TECNICO-COMPLETO.md:46-52
1. Spatial AI & 3D Processing Architecture
The core of TAMV's spatial capabilities lies in the Isabella AI Framework, specifically its Spatial AI module. This system is designed to understand 3D spaces with human-like or superior precision, utilizing specialized neural network architectures for semantic segmentation and object recognition.
1.1 Core Spatial Technologies
The system employs high-performance libraries and models to handle complex 3D data:
- ResUNet14: Used for robust feature extraction in 3D environments.
- MinkowskiEngine: Provides an engine for sparse 3D processing, allowing the AI to focus on relevant spatial data points without processing empty space.
- Trilateral CRF: Utilized for refined semantic segmentation of 3D scenes.
- ScanNet & SYNTHIA: These datasets, containing over 3,099 photorealistic scenes, serve as the training foundation for spatial recognition.
Sources: TAMV-MASTER-DOCUMENTATION.md:310-318, TAMV-OFFICIAL-SYSTEM-DESCRIPTION.md:27-35
1.2 Spatial AI Workflow
The following diagram illustrates the flow of spatial data from raw input to intelligent output within the DreamSpaces engine.
The diagram shows how 3D sensor data and point clouds are processed through MinkowskiEngine and ResUNet14, refined via CRF, and finally used for navigation and asset anchoring in DreamSpaces.
Sources: TAMV-MASTER-DOCUMENTATION.md:310-318, TAMV-OFFICIAL-SYSTEM-DESCRIPTION.md:92-104
2. DreamSpaces & Immersive Geolocation
The "DreamSpaces-engine" is a dedicated microservice that manages virtual worlds, events, and spatial physics. It integrates real-world geolocation data to anchor virtual assets (XR/VR) to specific physical coordinates.
2.1 Key Service Endpoints
The DreamSpaces engine exposes several endpoints for managing spatial and physical interactions:
/spaces: Management of virtual world instances./physics: Quantum physics simulations and collision detection./events: Spatial events and real-time triggers./multiplayer: Synchronization of multiple users within a shared spatial context.
Sources: TAMV-MASTER-DOCUMENTATION.md:126-130, REPORTE-AVANCE-TECNICO-COMPLETO.md:154-165
2.2 Data Model Integration
Geolocation is integrated into the user and asset models via the PostgreSQL database, which utilizes pgvector for handling spatial embeddings and coordinate-based lookups.
| Field | Type | Description |
|---|---|---|
location | String | Human-readable location description for user profiles. |
wallet_address | String | Used for blockchain-based spatial asset ownership. |
media_urls | Array | References to spatial media or XR filters stored in S3/IPFS. |
visibility | Enum | Controls access to spatial posts (Public/Followers/Private). |
Sources: TAMV-BACKEND-COMPLETE.md:175-186, TAMV-MASTER-DOCUMENTATION.md:94-102
3. Spatial System Integration & Functional Rules
The system follows strict functional rules for integrating spatial files into the core architecture. This ensures that XR and geolocation data are processed through audited pipelines.
3.1 Functional Integration Rules
The project defines specific rules for how spatial and XR files are enrouted within the backend:
- Layer: L4 - XR/VR/3D/4D.
- File Patterns: Identifies files matching
xr.*.ts,dreamspaces.*.ts, orrenderer.*.ts. - Target Directories:
backend/src/core/xr,backend/src/services, andbackend/src/routes. - Subsystem Hint: S1 Pipeline A – Visual XR/4D.
- Rationale: Favorable spatial files are enrouted to the visual pipeline only after undergoing semantic validation to ensure ethical compliance.
Sources: scripts/analyze-duplicates.ts:133-140, REPORTE-AVANCE-TECNICO-COMPLETO.md:154-165
3.2 System Interaction Sequence
The interaction between a user request for spatial data and the backend services is depicted below:
This sequence shows the validation of user identity via Anubis Sentinel before Isabella Spatial AI queries spatial embeddings from the database to return render instructions.
Sources: REPORTE-AVANCE-TECNICO-COMPLETO.md:183-207, TAMV-OFFICIAL-SYSTEM-DESCRIPTION.md:43-52
4. Conclusion
Geolocation and spatial features in TAMV represent a sophisticated blend of AI-driven 3D understanding and coordinate-based asset management. By utilizing ResUNet14 and MinkowskiEngine for processing, the system provides accurate semantic segmentation of environments, while the DreamSpaces engine ensures that virtual experiences remain anchored to verifiable physical or mathematical locations. This infrastructure is critical for the TAMV vision of digital sovereignty, ensuring that spatial data remains under user control and is processed according to established ethical guidelines.
Sources: TAMV-MASTER-DOCUMENTATION.md:310-318, TAMV-OFFICIAL-SYSTEM-DESCRIPTION.md:120-125