I’m excited to share an article about SegmentAnyTree, a sensor-agnostic deep-learning model capable of accurately identifying individual tree crowns from various laser scanning data sources, including airborne, terrestrial, and mobile platforms. This represents a significant advancement in the field, offering several key benefits:

• Enhanced understanding of forest composition: By precisely segmenting trees, we gain deeper insights into forest dynamics, enabling data-driven management practices.
• Adaptability across diverse environments: SegmentAnyTree performs well in a range of data densities and complex forest structures, ensuring its applicability in various real-world scenarios.
• Setting a new standard: Compared to existing methods, SegmentAnyTree demonstrates superior accuracy and efficiency, establishing a new benchmark for tree segmentation and paving the way for further advancements in ecological modeling.

The model was developed as a part of SmartForest4.0 at NIBIO Norwegian Institute of Bioeconomy Research.