What if we could use AI (artificial intelligence) and 3D forest reconstruction from remote sensing data to help find and reconstruct trees in a forest? Researchers at Purdue University’s Department of Computer Science and Institute for Digital Forestry and Germany’s Kiel University are doing exactly this, leveraging AI to isolate and reconstruct forest trees.
Up until now, existing algorithms could only partially reconstruct the shape of a single tree from a clean point-cloud dataset acquired by laser-scanning technologies. Now, researchers have introduced TreeStructor in IEEE transactions on geoscience and remote sensing.
Lidar (light detection and ranging) works by shooting laser pulses at the target objects, then detecting the reflected light. Tree trunks and branches standing behind the reflecting objects remain invisible, and the canopy dissipates the reflections to almost random directions. The workaround is to combine the results of multiple scans from various angles from the ground and sometimes from a drone flying above.
Here is how this can help:
- Detect and isolate repeating parts and capture tree shapes.
- Provide scientific research.
- Could lead to economic benefits in the future.
Looking to the future, this could open new opportunities for digital twins, forest reconstruction, and more, as we look to create images of shapes. Urban structures, furniture, cars and other human-built products display a high degree of symmetry, making them easier to detect from point-cloud datasets collected by lidar and other remote-sensing technology. But now we may have technology to help create similar datasets for nature.