Locating and profiling city street trees using Baidu street view images for carbon storage evaluation
Bibliographic record
Abstract
Traditional methods for estimating the carbon storage of street trees involve manual sampling, which incurs substantial human, material, and temporal costs in establishing a city-wide comprehensive inventory of street trees. In this study, we propose a multi-task convolutional neural network called STV-MNet to identify individual- level and city-wide street trees from Baidu street view images (BSVIs). We measured the structural and locational information of the identified trees using cylindrical projection and MonoDepth depth estimation network. STV-MNet achieved a mean intersection over union (mIoU) of 0.733 and a mean average precision of 0.881 at IoU 50 % (mAP50) in individual tree identification, outperforming DeepLab v3+ (mIoU of 0.641) and YOLO v3 (mAP50 of 0.767). Validation with street-measured data demonstrates that our method produces more precise estimations for both tree height and breast diameter, with the root mean square error (RMSE) of 0.09 m and the normalized RMSE of 0.005 m for tree height and the RMSE of 0.01 m and the normalized RMSE of 0.016 m for diameter at breast height (DBH). The location prediction of street trees achieves a minimum error of 0.67 m and an average error of 7.37 m. Using the biomass carbon storage equation, we calculated the carbon storage of individual street trees in Changsha City, Hunan Province, China. The results indicate that the total carbon storage of 333,717 street trees in urban areas of Changsha City is 1.64 × 10 5 tons, and the annual carbon sequestration capacity across the urban areas is 8014.57 tons. In certain areas, street tree resources have enabled the achievement of carbon neutrality in road transportation. This study presents a novel approach to managing urban street tree carbon storage, leveraging STV-MNet for automatic carbon storage estimates, and demonstrates high practical significance in low-cost and city-wide street tree carbon storage estimation. • STV-MNet is proposed for individual-level street tree identification from Baidu street view images. • The structural and location information of street trees were predicted in the urban areas of Changsha City. • The calculated total carbon storage of urban street trees in Changsha City is 1.64 × 10 5 tons.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".