Individual tree identification and visualization in forest ecosystems using LiDAR files and segmentation algorithms for forest management applications

Forests are essential ecosystems that provide crucial ecological, economic, and social benefits worldwide. However, monitoring individual trees within these complex environments poses significant challenges due to their scale, structural complexity, and often limited accessibility. This project introduces a comprehensive modular system for identifying and visualizing individual trees, leveraging LiDAR data and advanced segmentation algorithms to support sustainable forest management practices.

Based on a microservices architecture, the platform integrates Potree visualization capabilities with deep learning tools to enable interactive tree analysis. The system's algorithmic approach allows it to adapt to diverse forest types and conditions. The results demonstrate the project's scalability, accuracy in tree delineation, and potential for future expansion through integration with classification and biodiversity monitoring modules.