Understanding Representation Gaps Across Scales in Tropical Tree Species Classification from Drone Imagery

2026-04-24

AI Summary

Accurate species classification of tropical trees from centimeter-resolution drone-based nadir imagery remains challenging due to their high diversity and visual similarity. This work proposes a cross-scale self-supervised representation alignment method that integrates high-resolution close-range and canopy-level nadir images. It presents the first systematic quantification of the performance gap between these two imaging scales in species identification, revealing that the gap is especially pronounced for rare species. Leveraging vision foundation models and general-purpose plant recognition architectures, the approach combines fine-tuning with self-supervised learning to substantially improve classification accuracy at the canopy scale—particularly for rare taxa—thereby offering an effective technical pathway for large-scale monitoring of tropical forest biodiversity.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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. In this work, we evaluate the performance of existing methods using paired top-view and close-up UAV imagery collected in a species-rich tropical forest. Through fine-tuning experiments, we quantify the performance gap between vision foundation models and in-domain generalist plant recognition models across both image types (high-resolution close-up versus coarser-resolution top-view imagery). We show that classification performance is consistently higher on close-up images than on top-view aerial imagery, and that this performance gap widens for rare species. Finally, we propose that self-supervised representation alignment across these two spatial scales offers a promising approach for integrating fine-grained visual information into canopy-level species classification models based on top-view UAV imagery. Leveraging high-resolution close-up UAV imagery to enhance canopy-level species classification could substantially improve large-scale monitoring of tropical forest biodiversity.

Problem

• tropical tree species classification
• UAV imagery
• representation gap
• spatial scale
• biodiversity monitoring

Innovation

• self-supervised representation alignment
• cross-scale learning
• UAV imagery
• tropical tree species classification
• fine-grained visual recognition