Using AI to automate processing of point clouds
An accurate classification of a point cloud typically involves a lot of hand annotation. At Flai, we think that by utilizing the most recent advancements in artificial intelligence, all of those repetitive tasks may be significantly reduced. With a particular focus on point cloud datasets, we have created a cloud-based web application that offers simple-to-use solutions for the classification, analysis, and management of geographic data.
A group of professionals with skills in computer engineering, machine learning, geodesy, and physics came together to create Flai. We created Flai as a response to the rising need for the automation of routine geospatial operations and special requests by combining our expertise in processing vast amounts of data using the most recent technology with our awareness of customer demands. With the help of our solution, businesses using point cloud data may switch from labor-intensive manual processing to autonomous flows that are faster and more effective thanks to cutting-edge machine learning (ML) and artificial intelligence (AI) technologies. These strategies are essential for continuously producing precise items more quickly than ever.
Web application and data ingestion
All operations are accessible through the Flai web application to facilitate tool usage and to be independent of the underlying hardware and operating system on which they would be run. Many geographic datasets, including point clouds, rasters, vectors, and pictures, can be uploaded, browsed, and combined using the application. The datasets are securely stored in the cloud after being uploaded, and only the company that started the data ingestion process has access to them. The Flai environment can be implemented at the user's own computer facility when even more security is required and data cannot leave its nation of origin or the data-producing organization.
We also provide on-site batch processing services for certain jobs when processing huge amounts of data without using the online application.
The web application interface gives a user-friendly interface for developing and executing user-defined processing flows as well as simple access to submitted data. Each flow has the capacity to simultaneously output innumerable new datasets created from input data and merge numerous input datasets of various sorts. Users can select from a wide range of predefined processing tools, focusing primarily on point cloud datasets, ranging from straightforward operations like class remapping and filtering to complex operations intended to interpret the data and produce higher-level results comprehensible by a wider audience. The most popular technology is point cloud semantic segmentation, which takes unprocessed measurements and gives each Lidar point a valuable semantic label. The chosen area of interest and level of detail dictate how many labels are generated.
A completely annotated and ready-to-use dataset is created from a raw point cloud using the Flai automatic artificial intelligence system.
How does Flai point cloud classification work?
At Flai, we make an effort to carry out all of our data processing operations using the most recent methods and best practices. This is particularly clear in our point cloud categorization challenge, which makes use of cutting-edge ML and AI point cloud processing methods. Working with such data can be difficult due to its unordered structure and unique characteristics because even datasets that were obtained uniformly can have wildly varied densities and height spans. To avoid these possible dangers, we divided a dataset into several overlapping, small pieces, and gave each section special attention.
The precise cartesian coordinates, height above the ground, and additional Lidar attributes (intensity, return number, number of returns, and RGB values) are provided to a pre-trained AI model in order for the classification algorithm to comprehend relationships between points. Our team specified the meaning of the per-point classification labels based on typical client requirements and use situations, which are output by the model's internal computations.
The program provides ready-to-use AI models appropriate for mobile mapping, drone applications, large-scale mapping, and forestry inventory generation. Our team of data engineers carefully selected and expertly labeled a large pool of various point cloud scenarios for them to train on. Every time we come across a new kind of terrain, plant life, or structure, an ever-expanding set is added to. We also provide the opportunity to build user-tailored classification models that are trained on the user's data and categorization labels for more sophisticated and particular use cases. All of our users can use this functionality to experiment with their data and build unique models. In order to increase the accuracy of predictions, the training process also contains an interactive component that will recommend which data should be further categorized and added to a training set.
The Flai point cloud viewer provides a variety of tools for categorizing items in a point cloud so they can be quickly assigned to any user-defined label.
Manual point cloud annotation
Results from the automatic classification process can be examined, changed, and measured within the application. We integrated a user-friendly three-dimensional viewer for this use that enables users to easily fly over even the largest datasets. A variety of point selection methods can also be used to fix any discovered misclassifications. Strip, box, and polygonal selections are examples of those that can be mixed interchangeably to produce desired outcomes by remapping points of one or more labels into a new label. Virtual tiles that seamlessly divide the dataset into more manageable pieces can also be used to distribute the manual categorization effort across several users inside the user organization. Every annotator can start a dialogue by adding a note directly to a point cloud whenever they are having trouble settling on an item type.
How can I benefit from using Flai?
There are many different point cloud uses that go much beyond the mentioned classification task. Flai is continually creating and introducing new technologies that enable the extraction of more useful and manageable vector products from point clouds. Our team can also provide tailored solutions to streamline and quicken your existing workflows, making it simpler to digitalize tasks like surveying, urban planning, mining, and building sites.
Large-scale aerial mapping
Most significant Lidar acquisition firms still rely on semi-automatic scripts that can only extract the most fundamental and straightforward things from the acquired point clouds. They continue to use labor-intensive human-produced annotations for post-processing and the extraction of additional classification labels. Some of them are already replacing their outdated approaches with automated ones with the aid of Flai. Since the primary goal of large-area mapping datasets is the creation of digital elevation models, our primary responsibility for such broad mapping projects is to extract trustworthy ground representation free of any outliers. We can also give annotations for buildings, vegetation, bridges, water, powerline infrastructure, and all other remaining human-built structures, depending on what our customers need. Our AI models allowed for significant time savings for end users during the past year. According on the complexity of the use case, reported time savings for the manual annotation range between 30% and 80%.
Even in the most complicated situations, such as those involving overhangs, terraces, and areas with a lot of boulders, Flai offers dependable ground extraction.
Mapping with drones
The same methods can also be used to analyze unmanned aerial vehicles' (UAVs, or "drones") often significantly denser datasets. To understand a particular location, we concentrate on processing small regions that have been captured. These areas need high-frequency monitoring, which is typically impractical from the ground or would take too long to gather using ground measurements. As a result, using drone scanning in conjunction with our automated processes is the best way to produce trustworthy solutions on schedule and continuously for risk assessment and monitoring of critical infrastructure. Our customers are drawn from a wide range of industries, including mining, urban planning, landslide and rockface monitoring, transmission wire inspection, and many more. Instead of taking weeks to finish, as was the case in the past, our application has assisted numerous UAV mapping companies.
Forest inventory
A growing number of initiatives are attempting to generate the most accurate forest inventories possible in response to the rising demand for sustainable practices and the requirement to account for and monitor greenhouse gas sinks and sources. In large forests, only a few sample locations can be used because the process of extracting inventory information, such as tree size, diameter, and species, is often quite labor-intensive. There is an increasing need for extracting this information directly from point clouds to make the process simpler and give more accurate results. They are excellent sources for the estimation of biomass at the individual tree level when the point density is high enough and a sufficient number of Lidar points penetrate the canopy down to the ground level and strike tree trunks.
For a single tree delineation, the Flai solution may report all pertinent tree data, including tree height, canopy distribution, trunk length, and radius.
To make the transition easier, we have created a unique classifier that can separate the volume of the forest into the three crucial categories of tree canopy, trunks, and understory. They are used to produce precise maps of the positions and heights of individual tree tops, to trace the outline of the canopy, and to calculate vertical crown density profiles. We can also track tree trunks in three dimensions, construct radial profiles along their length, and calculate individual volumes thanks to single trunk categorization. The disclosed method has already aided numerous clients all over the world in releasing the full potential of AI and ML in applications for forest inventory and carbon trading.
Conclusion
The web application from Flai has proven effective for several use cases involving the analysis of point cloud data from various sources. Any type of data, from very high-density terrestrial scanning data to low-density aerial data, can be handled by our application. We provide a freemium plan with constrained processing resources for all of our current tools so that you can test them out risk-free and learn how Flai can benefit your company. Simply register for a free account on our website to try it out.
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