Flai

About Flai

Flai key features

• Fast and accurate point cloud classification using pre-trained AI models across various categories (e.g., aerial, forestry, mobile mapping).
• Advanced data processing with over 40 processors for comprehensive manipulation and generation of raster and vector deliverables.
• Custom classifiers that allow users to train tailored models for extracting specific features from point cloud data.
• Flexible deployment options, including cloud-based processing and self-hosted solutions.
• Tools for manual annotation, processing flows, and an online 3D viewer integrated into the platform.

Flai use cases

• **Wide Area Mapping**: Automatic classification of LiDAR point clouds for aerial mapping projects, including detection of ground, vegetation, buildings, and infrastructure.
• **Forestry Applications**: Efficiently conduct forestry inventory and carbon trading by classifying tree trunks, understory, and canopy using specialized AI models.
• **Power Line Monitoring**: Automatic detection and classification of powerline components, including wires and towers, to ensure safe and reliable operation.
• **Mobile Mapping**: Classification of mobile mapping point clouds to identify roads, sidewalks, traffic signs, and vehicles for urban planning and management.
• **Mining Services**: Monitoring and classification of open mines and indoor stockpiles using LiDAR data to track material volumes and detect terrain changes.

Useful for

• Enables fast and accurate classification of large LiDAR point cloud datasets, significantly reducing manual effort and time.
• Offers pre-trained AI models tailored for various industries, including forestry, mobile mapping, and power line monitoring, enhancing versatility and applicability.
• Provides advanced data processing tools with over 40 processors for comprehensive manipulation and generation of raster and vector deliverables.
• Allows for custom classifier training to extract specific features from point cloud data, catering to unique project requirements.
• Supports flexible deployment options, including cloud-based and self-hosted solutions, accommodating different operational needs.

Price

• Flai offers four distinct pre-trained classification models: Aerial Mapping AI model, Forestry AI Model, Mobile Mapping AI Model, and Indoor Stockpile AI Model.
• The Aerial Mapping AI model includes categories such as ground, vegetation, buildings, powerline towers, and more.
• The Forestry AI model classifies points into categories like ground, vegetation, tree trunks, and fallen trees.
• The Mobile Mapping AI model categorizes features including roads, sidewalks, traffic signs, and vehicles.
• The Indoor Stockpile AI model classifies points into stockpile and other man-made objects.