Multi-branch and multi-label tree species classification using deep learning for UAV aerial photography and Sentinel remote sensing images
Bibliographic record
Abstract
The classification and identification of forest tree species is of great value in the study of species diversity and forest monitoring. With the development of emerging technologies, the combination of remote sensing images and deep learning methods has become an important means to study multi-label image classification. However, nowadays, due to the small difference between tree species images, the difficulty of artificial labeling, and the difficulty of obtaining data sets, there are few studies on multi-label classification for tree species images. Therefore, taking the TreeSatAI dataset as an example, a multi-branch and multi-label image classification model (MMTSC) specifically designed for multi-source remote sensing data is proposed to classify and identify 15 tree species in the dataset. In a complex forest stand scenario with unbalanced data, our F1-Score and Precision are as high as about 72% and 82%, respectively. The visualization results of the confusion matrix and Grad-CAM heat map further verify the model's recognition ability on different categories. To comprehensively evaluate the model performance, we compared it with other state-of-the-art (SOTA) methods for multi-label image classification tasks and conducted a series of ablation experiments. Experimental results show that the MMTSC model outperforms other SOTA methods in F1-Score, Precision, Recall, and mAP. In addition, we also compared the model's backbone network DenseNet121 with the classic structures of EfficientNet-B0, ConvNeXt-Tiny, ResNet-18, MobileNetV3 and RegNetX-800MF. The evaluation results showed that the DenseNet121 architecture performed best in this task, verifying its effectiveness and adaptability as a backbone network. Finally, we use the results of the deep learning-based multi-label tree species classification model for biomass estimation, providing practical suggestions for relevant institutions, thereby contributing to the scientific management of forest resources and the improvement of carbon sequestration capacity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".