Object-based larch tree-crown delineation using high-resolution satellite imagery
Bibliographic record
Abstract
AbstractOlgan larch is a traditional construction material used for the renovation of historical timber-frame buildings in China. However, acquiring the necessary large-sized larch trees from old-growth forests has become a challenge in China because of the rare and inaccessible distribution of these trees. In recent years, remote sensing imagery has provided a more effective alternative for delineating tree crowns automatically with high accuracy. In this study, an object-based method for delineating old-growth larch tree crowns using Geoeye-1 imagery is developed. Tree crown delineation results are tested and evaluated by field data. In addition, the correlation between delineated tree-crown and basal areas are quantitatively validated to ensure that the developed method can be applied for estimating the distribution of old-growth larch trees. Results demonstrate that the developed object-based larch tree-crown delineation method is reliable, thus providing a new technique for detecting old-growth larch tree resources in Northeastern China. Additional informationFundingThis study was supported by The Fundamental Research Funds for the Central Universities [BLX2013042]; Special Fund for Basic Scientific Research Programmes of the Central Universities [No. TD 2011-32]; and Grant-in-Aid for Scientific Research, Scientific Research-A [No. 20240074] (2008–2010), [No. 23240113 (2011–2013)] from the Japan Society for the Promotion of Science.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".