Turning point clouds into geospatial intelligence
Juglans is an advanced, automatic LiDAR point cloud classification technology powered by artificial intelligence that enables us to transform large volumes of LiDAR data into precise, up-to-date geospatial information that is ready for use. Juglans combines advanced deep learning algorithms with specialized quality control processes to ensure reliable results.
Juglans is designed to efficiently process large areas while maintaining the quality and consistency of the results. With Juglans, we can automatically classify multiple land features, including soil; low, medium, and high vegetation; buildings; bridges; solar panels; wind turbines; power lines; power towers; antennas; bodies of water; vehicles; and dams and dikes.
Juglans is particularly useful for creating highly precise 3D representations of any object on the ground, which can be applied to a wide range of fields, including advanced mapping, infrastructure monitoring, environmental management, urban planning, risk and emergency management, and more.
With Juglans, we transform complex point clouds into structured geographic information that streamlines decision-making. Our blend of geospatial expertise and knowledge of LiDAR and AI enables us to deliver Juglans as a faster, more accurate, and more scalable product. It is designed to be useful for public administrations, mapping agencies, and infrastructure companies, among others. Juglans significantly reduces costs and production times compared to traditional methods. It also allows one to integrate other geospatial sources to enhance accuracy.
Juglans use cases
Classification of infrastructure areas
Point clouds of an area in Navarre, showing the Sancho el Mayor highway bridge over the Ebro river (purple), the towers and power lines of the REE substation (yellow and blue) and riparian trees (green).
Classification of urban environment elements
In this example, which captures point clouds in an urban area, building roofs stand out in purple, trees in green, vehicles in yellow, and urban paving in blue.
