Spatiall AI

Turning raw data into rich, usable insights.

Spatiall AI

Spatiall AI revolutionizes public space management by providing scalable, digital asset intelligence that empowers professionals to make confident, data-driven decisions.

Through advanced extraction of location, inventory, and inspection details from imagery and LiDAR point clouds, Spatiall AI replaces manual processes and fragmented records with reliable, actionable insights.

Key Features

AI-powered object detection on panoramic imagery and point clouds
Combines visual and spatial analysis for accurate classifications
AI models evolve through customer feedback and quality checks
Supports version control, time comparison, and change detection
Ready for integration with GIS platforms, asset management systems, or custom viewers
Supports multiple levels of detail based on project requirements

Asset types

Traffic signs

AI-InfraSolutions offers an automated solution for detecting and classifying traffic signs.

This system utilizes proprietary Mobile Mapping vehicles and AI technology to collect detailed information about the location, condition, and type of traffic signs. The data is processed in Spatiall Studio and can be delivered in 2D or 3D formats, contributing to sustainable management and traffic safety. Additional information, such as damage or aging over time, can also be recorded.

Light poles

We also offer automated inspection and analysis of light poles.

Based on the data the condition and location of light poles can be accurately detected and recorded. This helps in efficiently planning maintenance and enhancing public safety by ensuring proper lighting functionality.

Utility poles

We also offer automated inspection and analysis of light poles.

Road markings

This solution focuses on the automatic detection and analysis of road markings.

Using AI and image processing technology, road markings are accurately identified, classified, and assessed for quality. This ensures their visibility and effectiveness, which are crucial for traffic safety. The collected data can be used for maintenance planning and ensuring an up-to-date road infrastructure.

Trees

Our AI Lens for trees offers automated detection and analysis of trees, including their location and size.

Using AI and sensor technology, precise data is collected to support urban green space management and infrastructure project planning. The results can be integrated into 2D and 3D visualizations, providing valuable insights for sustainable green management and ensuring safety around trees

Surface types

Our AI Lens for surface layers provides automated terrain classification, delivering valuable data for urban planning, infrastructure development, and environmental analysis.

By leveraging AI and LiDAR data, terrain and surface types can be accurately detected and classified, enhancing efficiency in project planning and promoting cost savings. The results can be delivered in 2D, 3D, or as a data layer in GIS systems, making it essential for projects such as Fiber-to-the-Home initiatives.

Street furniture

Benches, bollards, bins, bike racks, and other street furniture shape how public space is used and experienced. These elements influence accessibility, safety, and spatial quality, but public authorities often lack a complete and up-to-date inventory.

Our AI scans and classifies all visible pieces of street furniture along the captured route, providing data to support design, maintenance, and urban planning.

Drains and manholes

Drainage systems are essential to keep our cities safe and hygienic, with gulleys and channels draining rainwater into the sewers. But it is important for operators to know if aging sewer systems have enough local drainage capacity. Or how many drains they need to effectively manage the system.

Our mapping technology detects and maps every visible gulley, providing location and type. It also detects all visible manholes, to give operators an understanding of how many sewer access points they have for maintenance and inspection, and where they are.