SilvaScenes: Tree Detection and Species Classification from Under-Canopy Images in Natural Forests
arXiv:2510.09458v2 Announce Type: replace-cross Abstract: Interest in forestry automation is growing alongside rapid advances in deep learning. In particular, tree detection and taxonomic classification are seen as core tasks required for automating field surveys and forestry equipment. These operations must often be performed in under-canopy settings, which pose challenging conditions for perception systems, including heavy occlusion, variable lighting, and dense vegetation. Despite this necessity, current work has yet to properly establish the feasibility of simultaneously executing tree det
Advances in deep learning are enabling automation in fields historically reliant on manual labor, with forestry being a prime example where previously challenging conditions are becoming tractable.
Improved tree detection and species classification automate critical forestry tasks, leading to more efficient resource management, better ecological monitoring, and potentially reduced operational costs.
The ability to accurately perform tree surveys and classification in challenging under-canopy environments shifts forestry from manual, labor-intensive processes towards automated, AI-driven operations.
- · Forestry companies
- · Environmental monitoring agencies
- · Hardware manufacturers for forestry automation
- · AI/Computer Vision developers
- · Manual forestry survey labor
- · Traditional forestry equipment manufacturers
Increased efficiency and precision in timber harvesting, inventory management, and disease detection within forests.
Reduced operational costs for forestry, potentially leading to more sustainable practices and higher economic returns from forest resources.
The development of fully autonomous forestry operations, integrating AI perception with robotic platforms for harvesting and reforestation.
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Read at arXiv cs.AI