Understanding Representation Gaps Across Scales in Tropical Tree Species Classification from Drone Imagery
Accurate classification of tropical tree species from unoccupied aerial vehicle (UAV) imagery remains challenging due to high species diversity and strong visual similarity among species at typical image resolutions (centimeters per pixel). In contrast, models trained on close-up citizen science photographs captured with smartphones achieve strong plant species classification performance. Recent advances in UAV data acquisition now enable the collection of close-up images that are spatially registered with top-view aerial imagery and approach the level of visual detail found in smartphone photographs, with the trade-off that such high-resolution photos cannot be acquired for many trees. I...
| Authors | Sulagna Saha, Arthur Ouaknine, Etienne Laliberté et al. |
|---|---|
| Primary category | cs.CV |
| Published | 24 April 2026 |
| Last updated at source | 24 April 2026 |
Models and datasets referencing this paper
- sulagnasaharasha/bci-temporalDataset
- antitashi/bci-temporalDataset
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