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Explore Flai's recent innovations in automated LiDAR point cloud classification and processing through real-world use cases. Learn how to navigate our platform effectively with detailed guides for an optimized user experience, and see Flai in action.
How to Keep Original (Ground) Labels Unchanged When Using Flai Classification
When you already have valuable labels in your point cloud data, you should not have to lose them when you run a new classification. This guide shows how to create a processing…
Pushing the Boundaries of Situational Awareness at REPMUS 2025: Our AI-Driven REA Journey
This September, we were proud to participate in REPMUS 2025 — Robotic Experimentation & Prototyping using Maritime Unmanned Systems — as part of the Rapid Environmental Assessment…
Flai in QGIS: open data and AI analysis one click away
QGIS is a leading open-source desktop GIS for Windows, macOS and Linux, used daily for cartography, spatial analysis, and 2D/3D data editing and publishing \[1\]. With its core…
Urban Tree Detection in Ljubljana - Using Field Surveys and LiDAR
Urban trees are vital to the health and sustainability of city environments. They provide a range of ecosystem services, such as cooling urban areas, filtering air pollutants,…
Automated Power Line Classification and Vectorization in Flai web app
In the rapidly evolving energy infrastructure landscape, power line management presents unique challenges for utilities and grid operators worldwide. The intricate network of…
Five LiDAR Software Tools: From Flight Planning to Classification
LiDAR (Light Detection and Ranging) has become a cornerstone in geospatial data collection—used for everything from forestry and agriculture to urban planning and archaeology. But…
Introduction to LiDAR Point Cloud
LiDAR (Light Detection and Ranging) technology has become a cornerstone in fields like autonomous driving, robotics, environmental mapping, and construction. At the heart of this…
Point cloud classification with machine learning: A short guide
Have you ever wondered how self-driving cars "see" the world? Or how robots understand 3D spaces? One key technology behind this magic is called point cloud classification. It…
How to thin the ground layer using Flai web app
In LiDAR (Light Detection and Ranging), the laser beam interacts with surfaces in three main ways: absorption, transmission, and reflection. Most LiDAR systems rely on reflected…
Basics of point cloud processing with AI
Point clouds are essential in 3D modeling, robotics, autonomous driving, and many other fields. They represent spatial data as a collection of points in three-dimensional space,…
How to improve point cloud project management
Managing point cloud projects effectively requires a combination of organization, the right tools, and a clear workflow. Since point clouds are large datasets representing 3D…
Applications of LiDAR in agriculture
Agriculture, one of humanity's oldest endeavours, uses state-of-the-art technologies to meet the challenges of a growing world population, climate change and limited resources. As…
Top free LiDAR data sources
LiDAR technology has rapidly become a tool in mapping, forestry, urban planning, disaster management, and many other fields. What was once an expensive and niche technology is now…
Challenges and opportunities in point cloud data processing
Point cloud data, a collection of spatial data points representing the surface of objects or environments, has become a cornerstone in various fields like 3D mapping, autonomous…
5 best LiDAR processing software
LiDAR technology has revolutionized the way we gather and analyze spatial data, offering unparalleled precision and versatility across various industries. From creating detailed…
Five best cloud-based GIS tools
As access to data collection becomes easier, the sheer volume of geospatial data being collected is skyrocketing, creating new challenges around storage and processing. Storing…
LiDAR Tech Trends in 2024
LiDAR technology has rapidly evolved from niche applications to mainstream adoption across multiple industries. LiDAR is no longer just about measuring distance - it's a key…
Automating LiDAR data workflows
LiDAR technology has transformed the way we map terrain and collect spatial data, becoming more accessible as hardware advances and costs fall. However, despite its growing…
How to become a GIS consultant
In today’s data-rich world, Geographic Information System (GIS) consultants are key players in helping organizations make informed, data-driven decisions by analyzing and…
Power Grid Vegetation Analysis with LiDAR and AI
ELES faced a significant challenge in monitoring and predicting vegetation growth near power transmission lines to ensure the safe, reliable, and uninterrupted flow of…
Top Advanced Tools Every Geospatial Consultant Needs
In today's rapidly evolving field of geospatial consulting, the importance of leveraging advanced tools cannot be overstated. Whether you're handling complex datasets, performing…
Forest Damage Assessment Template: Powering Forest Health Monitoring with AI
The Forest Damage Assessment Template leverages AI to assess forest damage using automated classification of LiDAR point cloud data.
Forestry Classifier Template: Leveraging AI for Automated Forestry Data Classification
Forest inventory is crucial for monitoring forest health, biodiversity, and carbon sequestration. However, traditional methods are often time-consuming and labor-intensive. The…
Mobile Mapping Classification Template: Leveraging AI for Automated Mobile Mapping Data Classification
The Mobile Mapping LiDAR Classification Template streamlines the process of classifying LiDAR datasets from mobile mapping, making it easier to analyze large volumes of data in…
How to grow a geospatial consultancy
We are capturing more data than ever before. Data is captured from various sources, including satellites, planes, drones, cars, boats, and terrestrial surveys. These platforms…
Batch Processing on Flai web app
Alongside the classical use of the Flai web application, Flai enables faster processing of point cloud data through Batch Processing.
Elevating Precision: Introducing Point Cloud Quality Control Service
In the ever-evolving landscape of 3D mapping and analysis, the integrity and accuracy of point cloud data are paramount. Recognizing the critical need for precision, we're…
Running Flai flows on your local machines
Among other deployment options, Flai supports running the automatic classification flows on your hardware, keeping all data private to your local environment. To run Flai flows…
Classify point clouds through APIs
In this blog post, we will delve into how API integrations are transforming interactions with AI technologies in point cloud processing. We'll demonstrate how these integrations…
Forest damage assessment from LiDAR point cloud using Artificial Intelligence
On Monday, July 24, wind gusts exceeding 200 km/h swept through the city of La Chaux-de-Fonds in northwestern Switzerland. The winds left devastating damage on buildings and in…
Automatically classify mobile mapping LiDAR datasets with ease
The need for regularly updated and accurate geospatial data has grown exponentially in the last decades. Mobile mapping LiDAR data serve as an important source for various…
Online sharing of point clouds using Flai Web Application
In today's data-driven world, seamless collaboration and sharing of datasets are vital for efficient analysis and decision-making. Flai, a robust web application, offers a…
Interpret your data by combining multiple sources
Looking at objects in geospatial data taken from one angle or by only one particular sensor can be challenging, especially when handling data sets of unfamiliar regions or scenes.…
Enhance Your Workflow with Flai's Processing Flow Templates
Flai's web application offers over 36 raster and point cloud flow processing nodes, providing a comprehensive set of tools for your data processing needs. To simplify the creation…
Using AI to improve forest inventory
Forestry inventory is a crucial aspect of sustainable forest management. It involves collecting data on the characteristics of a forest, such as height, diameter, age, and overall…
Leverage AI for automated point cloud processing
The creation of an accurately classified point cloud usually requires tremendous input from manual annotation. At Flai we believe that all those repetitive procedures can be…
Creating rasters from point clouds with Flai web app
Creating fully annotated point clouds is usually only the first step to producing a product that can be used and understood by a wider audience. If you want to create raster…
Using LiDAR point clouds in precision forestry applications
LiDAR proved to be a suitable technology for forestry applications as it can penetrate the canopy and accurately detect tree structure and ground points.
Scheduled maintenance - 29.12.2022
We wanted to let you know that Flai web application will be down for scheduled maintenance, starting on 29.12.2022 at 03:00 (CET) and ending on 30.12.2022 at 22:00 (CET).
Introducing Flai Web App: Automatic point cloud classification platform powered by AI
We are excited to announce the launch of our Flai Web App, a platform that automates point cloud classification using advanced AI models.
Automatic extraction of ground points of a rockface using Artificial Intelligence
In this project, the goal was to get ground points of a densely vegetated rockface to analyze the rock structure. This task could only be done by using a high-density LiDAR point…
How to use annotation tools in Flai web application
Point cloud datasets produced by 3D laser scanning or photogrammetry gain a lot of value if they are accurately classified.
Evaluation of FLAI AI Classification on LiDAR Data Collected with the AISPECO Heliux LITE System and RIEGL VQ-580 II-S
The purpose of this document is to evaluate how FLAI’s AI-based point-cloud classification performs on LiDAR data collected with the AISPECO HELIUX LITE airborne system equipped…