Tree-species recognition with LiDAR: limited cross-source generalization revealed
In brief
- A new LiDAR benchmark (FOR-species20K) compiles 25 datasets to classify 33 tree species.
- The ConvNeXtV2 multi-resolution model reaches 85% accuracy in controlled conditions, up from 79.5%.
- Cross-source generalization drops to 36% in real-world conditions, exposing data biases.
A new study evaluates tree-species recognition using LiDAR data from 25 distinct datasets. While the proposed model improves performance in controlled settings, it shows major limitations when generalizing across different data sources.
What it changes for you
- AgricultureLiDAR-based tree species recognition tools must include diverse datasets to avoid costly biases in forest management.
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