Using a VGG-16 Network for Individual Tree Species Detection with an Object-Based Approach
Bibliographic record
Abstract
Acquiring information about forest stands such as individual tree species is crucial for monitoring forests. To date, such information is assessed by human interpreters using airborne or an Unmanned Aerial Vehicle (UAV), which is time/cost consuming. The recent advancement in remote sensing image acquisition, such as WorldView-3, has increased the spatial resolution up to 30 cm and spectral resolution up to 16 bands. This advancement has significantly increased the potential for Individual Tree Species Detection (ITSD). In order to use the single source Worldview-3 images, our proposed method first segments the image to delineate trees, and then detects trees using a VGG-16 network. We developed a pipeline for feeding the deep CNN network using the information from all the 8 visible-near infrareds' bands and trained it. The result is compared with two state-of-the-art ensemble classifiers namely Random Forest (RF) and Gradient Boosting (GB). Results demonstrate that the VGG-16 outperforms all the other methods reaching an accuracy of about 92.13%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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) | 0.004 | 0.002 |
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".