Solving business problems with AI-enhanced LiDAR data
The use of LiDAR data can create many opportunities to solve problems requiring spatial information. In an earlier blog post we dug deeper into how machine learning models can analyze 3D point clouds for several tasks such as classification, object localization, and object segmentation. This post will explore and give examples of how these techniques can be applied to solve real business challenges.
A quick primer on LiDAR sensors
LiDAR, or Light Detection and Ranging, is a remote sensing method that uses light in the form of a pulsed laser to measure distances. This technology generates precise, three-dimensional information about the shape of the ground and its surface characteristics. The output, known as a point cloud, consists of a large number of points that represent the surface coordinates, appearing as a dense cluster of dots that collectively depict the depth and form of the physical environment.
There's no doubt about the superiority of LiDARs for obtaining high-quality spatial data. Especially now that the price of this technology is affordable for various use cases. However, the systems integrating LiDARs might face an issue of overflow of unlabeled data to be processed, a problem solvable by processing the data with AI.
Mapping the cityscape
LiDARs produce thousands of points per second, but not all of these are relevant to the application used. Imagine taking a look at a city from a bird’s view with an object of counting all cars. The majority of the objects seen, such as buildings, parks, and pedestrians, might not be anyhow relevant to the task. The same applies to LiDAR data, where in many cases only a minority of the data is important for the given task.
AI-enhanced systems can automate the majority, if not all, of the object recognition and movement tracking tasks, even in real-time and in complete darkness. AI systems can also be taught to filter unwanted elements in the point cloud such as noise from rain or snow. This allows the LiDAR systems to gather data no matter the weather.
Depending on the training of the AI-enhanced systems, these can offer highly flexible or very strict recognition. Coming back to the example of the city view, the AI could recognize all cars, trucks, and buses, or just convertible cars if necessary. This level of specificity is particularly useful in industries where precision is critical, such as logistics and urban traffic management.
Access to high-quality spatial data with the possibility to strip certain features in real time opens up new business opportunities. By focusing only on relevant data, companies can optimize resources and improve service delivery.
More examples
Different industries would benefit from AI-enhanced LiDAR systems. Here are some examples of how LiDAR data can be used in different cases.
Forestry and Agriculture: Point clouds can be leveraged for biomass estimation. Suitable LiDARs are accurate enough to estimate the growth of individual crops. This precise measurement allows for better management of forests, planning of harvests, and monitoring of ecosystem health. Additionally, in agriculture, LiDAR can help map field topography, enabling precise irrigation planning and terrain analysis to maximize crop yield. Furthermore, having an accurate elevation map can help improve route planning through a forest and monitor ground erosion.
Urban Planning and Infrastructure: In urban settings, LiDAR combined with AI proves invaluable in managing and analyzing traffic patterns and human flow. By creating detailed 3D maps of urban areas, planners can anticipate traffic jams and optimize traffic flow based on real-time data. Moreover, AI-enhanced LiDAR can assess flood risks by analyzing terrain and elevation data to predict water flow paths in extreme weather, aiding in the design of more effective water management and flood defense systems.
Factory monitoring: LiDAR data is inherently private compared to the use of cameras. This opens opportunities to track people's movement in a room without infringing on their privacy. This way we can track current processes and use this data to improve efficiency. Additionally, the spatial resolution is much better than a camera’s. This can potentially be used for full automation helping with object detection and optimized routing for autonomous robots.
Getting started with AI and LiDAR sensors
Exploring how LiDAR technology works with AI shows us its big impact across different areas like forestry, city planning, and managing infrastructure. LiDAR, enhanced by AI, helps businesses handle large amounts of spatial data quickly, focusing on the important bits to make smarter decisions. For example, it can help manage forests better or improve how traffic flows in cities, making sure the data used is accurate and specifically suited for the task.
Looking forward, the combination of LiDAR data and AI techniques is promising. It could not only improve current ways of doing things but also completely change industries. Wondering if LiDAR technology could help you and your business? Get in touch and we can help you explore the possibilities.
Emblica is not your average data team. We build customized solutions for collecting, processing, and utilizing data for all sectors, especially at the R&D interface. Whether our target is a factory line, an online store, or a field, you can find us busy at work, hands in the clay - at least at our office in Helsinki.