Abstract of the Offer
UK-based dual-use technology provider offering a sensor-agnostic 4D geospatial digital twin in Unreal Engine, fusing optical, hyperspectral, RF, IR, and DEM data for real-time, AI-ready visualisation. Supports defence, infrastructure, environmental monitoring, and robotics integration. Advantages include global sensor fusion, secure NATO-aligned design, and immersive simulation. Seeking ESA co-development, licensing, and pilot project partners in digital twin targeting and critical undersea mapping and monitoring.
Description
This sensor-agnostic 4D geospatial digital twin platform integrates multiple data sources—optical, hyperspectral, RF, IR, and DEM—within an advanced simulation environment to create dynamic, time-enabled virtual representations of geographic space. It fuses, renders, and animates environmental and infrastructure data in three spatial dimensions plus time, enabling users to visualise real-time or historical changes.
Functions: The platform ingests and synchronises satellite and ground-based sensor inputs into a coherent geospatial-temporal framework. It can model environments at local to global scales, animate features and assets over time, support programmable workflows, interface with robotics and autonomous systems, and provide analytical visualisation for research, planning, or operational decision-making.
Technical Concept: High-fidelity data fusion and rendering algorithms normalise and align heterogeneous sensor formats. Multi-modal layers (volumetric, raster, vector) are composited for flexible visualisation. Temporal animation links assets to time-stamped attributes for playback, simulation, or prediction, with PNT data ensuring spatial accuracy. Modular and extensible architecture supports varied sensor inputs, mission profiles, and analytical tools.
Applications:
Security: Mission planning, asset tracking, situational awareness.
Infrastructure Management: Pipeline, power grid, and transport monitoring.
Environment: Observation and analysis of natural phenomena.
Disaster Response: Scenario rehearsal, rapid change assessment.
Urban Planning: City-scale modelling for optimisation and services.
R&D: AI, robotics, and autonomous system testing in realistic virtual environments.
Education & Training: Immersive environments for geospatial and robotics learning.
Resource Management: Geological modelling, exploration, restoration planning.
In practice, it transforms complex multi-source sensor data into actionable insight through immersive, interactive spatial-temporal modelling for both civilian and defence sectors.
Advantages and Innovations
The robotics geospatial map uses sensor-agnostic 4D digital twin technology within advanced simulation environments, integrating satellite, hyperspectral, RF, IR, and DEM data. It fuses heterogeneous sensor inputs into interactive, time-enabled geospatial visualisations for AI-driven robotics workflows and scenario simulation.
It ingests, aligns, and animates data in spatial and temporal dimensions, enabling historical replay, future event simulation, and environment modelling. Data fusion algorithms handle multi-format inputs, while PNT integration supports precision for robotics testing. Modular control nodes allow distributed, programmable access for collaborative missions.
Advantages over current methods:
Performance: Real-time updates and predictive analytics can boost operational efficiency by ~25%.
Cost: 20–30% savings through reduced site visits, remote collaboration, and virtual inspections.
Safety: Simulations enable better risk assessment, especially in hazardous or remote areas.
Ease of use: Continuously updated, cross-device access with cloud integration for rapid decisions.
Scalability: Handles large datasets from global to local missions.
Integration: Robotics-ready architecture supports automation, legacy interoperability, and custom app development.
In summary, it advances beyond static models to deliver real-time, high-fidelity geospatial intelligence for enterprise, infrastructure, and defence operations.