Remote sensing


The advancements in technology have revolutionized various fields, including forest management and research. We are exploring the potential of using drone-based point clouds and harnessing machine learning and other AI techniques for precise forest measurement and experimental plantations. The use of drones in this context offers a complementary approach to traditional methods and enables efficient data collection and analysis.

Machine learning

The primary benefit of using machine learning methods is the ability to automate the data processing, thereby reducing manual labor and the time required for analysis. By training models on large datasets of labeled point clouds, AI algorithms can learn to classify tree species accurately, distinguish between vegetation and non-vegetation points, and estimate tree heights. These automated processes enable quick and cost-effective assessment of forest composition, making it easier to monitor changes over time and optimize forest management strategies.

Point clouds

Point clouds generated from drone-based remote sensing provide a three-dimensional representation of forest areas. These point clouds contain valuable information such as tree height, crown structure, and species distribution. Using machine learning algorithms and AI methods, these point clouds can be processed and analyzed to extract relevant forest inventory parameters and gain insights into the health and characteristics of the forest ecosystem.


Furthermore, the integration of drone-based point clouds with AI methods opens up new possibilities for monitoring experimental plantations. Researchers can leverage the detailed information captured by drones to assess the growth and development of different tree species, evaluate the effectiveness of silvicultural treatments, and identify factors influencing planting success. The ability to measure tree heights and identify species from the point clouds enables researchers to conduct precise analyses and make data-driven decisions regarding forest management and conservation.