What is Spatial AI? Defining the field: Part One.
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5 min read
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Jan 7, 2024
Spatial AI refers to the integration of artificial intelligence (AI) techniques and technologies with spatial data and processes.
It involves leveraging AI algorithms to analyse, interpret, and derive insights from data that has a spatial or geographic component. This field combines the power of AI, machine learning, and data analytics with geospatial information, enabling systems to understand, reason about, and make decisions based on spatial relationships and patterns.
For the graphically minded, Spatial AI is a new term for a new and emerging field created by the overlap of other emerging fields.
- At its simplest, Spatial AI is the Venn Diagram where Spatial Computing and AI overlap — the intersection of Spatial Computing and Geospatial on one side, and AI/ML and Data Science on the other side.
- Importantly, there is a third field that interacts with both these, which brings the human element to what could otherwise be a very clear cut scientific definition:
By considering the human element, Spatial AI takes on new meaning as the highly personal tools and interfaces we will be using in the coming years to interact with the physical world — with an added digital overlay.
Concepts and tools like VR, AR and XR are already heading in this humanistic direction.
As a quick recap:
- VR is Virtual Reality — where a headset replaces a users entire world view;
- AR is Augmented Reality — where a headset can overlay information into a users world view;
- XR is Extended Reality — the same concept as AR, ie: a mixture of the digital world overlaying the real world — but with an emphasis on tactile experiences using low-fidelity (‘lo-fi’) objects to project the overlays onto for enhanced believability.
- While VR assumes the user is in a fixed (safe!) location, AR and XR will slowly encourage people to explore their surroundings, and interact with others in a combination of physical and digital modes.
VR/AR/XR are only vehicles into the world of Spatial AI- not in and of themselves Spatial AI. Spatial AI can encompass far more facets than simply interactive headsets.
Software/Conceptual Taxonomy of Spatial AI
In practical terms, Spatial AI encompasses a wide range of concepts and scientific fields, including but not limited to:
Geospatial Data Analysis: Using AI algorithms to analyse and interpret data derived from sources like satellite imagery, GPS, and geographic information systems (GIS).
Spatial Reasoning: Enabling AI systems to understand and reason about spatial relationships, including 3D spatial awareness, object recognition, and scene understanding.
Location-based Services: Integrating AI into services that rely on location information, such as personalised recommendations, navigation, and context-aware applications.
Autonomous Systems: Applying spatial AI in autonomous vehicles, drones, and robotics for tasks like navigation, object detection, and decision-making in dynamic environments.
Urban Planning and Smart Cities: Utilising AI to optimise city planning, infrastructure management, and resource allocation in urban environments.
Precision Agriculture: Using AI to analyse spatial data for optimised crop management, yield prediction, and resource utilisation in agriculture.
Natural Disaster Prediction and Response: Employing spatial AI for early detection, prediction, and efficient response to natural disasters using geospatial information.
Spatial-Temporal Modeling: Developing AI models that consider both spatial and temporal dimensions for predictive modeling and analysis.
And for completeness — Augmented Reality (AR) and Virtual Reality (VR): Integrating Spatial AI into AR and VR applications for enhanced object recognition, spatial mapping, and immersive experiences.
But really — we haven’t even begun to scratch the surface of the new and emerging concepts that lie within the bounds of Spatial Computing and AI. For instance, we need to consider the array of hardware and tools that support these conceptual fields.
Hardware Taxonomy of Spatial AI
The following is by no means an exhaustive list, but it begins to show that there is a growing ecosystem of physical components and system that are also emerging in their own right, in support of future Spatial AI technologies:
- Geospatial Sensing Devices: LiDAR Sensors, Radar Systems, Hyperspectral Imaging Devices, and Inertial Measurement Units (IMUs)
- Spatial Processing Units: Graphics Processing Units (GPUs), Tensor Processing Units (TPUs), Field-Programmable Gate Arrays (FPGAs), and Application-Specific Integrated Circuits (ASICs)
- Robotics Hardware for Spatial AI: Spatially Aware Robotic Sensors, Robot Arms and Manipulators, Locomotion Systems (e.g., Wheels, Legs), and Sensor Fusion Hardware
- Autonomous Vehicles Hardware: LiDAR and Radar Arrays, GPS and IMU Integration, Cameras for Environmental Perception, and Control Systems/Actuators
- AR and VR Hardware for Spatial AI: AR Glasses and Headsets, Spatial Cameras for AR, Haptic Feedback Devices, and Spatial Audio Hardware
- Edge Computing Devices for Spatial AI: Edge AI Processors, Edge Servers and Gateways, IoT Devices with Spatial Sensors, and Edge Inference Accelerators
- Custom Spatial AI Hardware Platforms: Modular Spatial AI Systems, Hardware-in-the-Loop (HIL) Simulation Platforms, Multi-Sensor Integration Platforms, and Portable Spatial AI Devices
- Spatial Data Storage and Retrieval Hardware: Spatial Databases and Servers, High-Performance Storage Systems, Geospatial Data Processing Units, and Data Retrieval Accelerators
- Communication Hardware for Spatial AI Systems: High-Bandwidth Communication Interfaces, Wireless Sensor Networks, Vehicle-to-Everything (V2X) Communication Devices, Low-Latency Communication Hardware
- Energy-Efficient Spatial AI Hardware: Low-Power GPUs and TPUs, Energy-Efficient LiDAR and Camera Systems, Edge AI Processors with Power Optimization, and Sustainable Hardware Designs
…there’s clearly a lot of topics within Spatial AI that require their own definitions and exploration.
Don’t panic — we will return to this topic in this week’s Podcast, and no doubt continue to tease out exactly where the edges of Spatial AI really are.
At SPAITIAL, we’ve intentionally put ourselves in the middle of these emerging fields, to be able to help chart a pathway into this new technology — and to explore the new ways of working as they present themselves.
Please subscribe to our weekly Podcast on whichever service you use for consuming audio — and join our online Community to join with others who share the same fascination with following this fast-paced field.
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