Bibliographic record
Abstract
Planning and managing urban trees and forests for livable cities remains an outstanding challenge worldwide owing to scarce information on their spatial distribution, structure and composition. Sources of tree inventory remain limited due to a lack of detailed and consistent inventory assessments. In practice, most municipalities still perform labor-intensive field surveys to collect and update tree inventories. This thesis examines the potential of deep learning to automatically assess urban tree location and species distribution from street-level photographs. A robust and affordable method for detecting, locating, classifying and ultimately, creating detailed tree inventories in any urban region where sufficient street-level imagery is readily available was developed. The developed method is novel in that a Mask Regional Convolutional Neural Network is used to detect and locate tree instances from street-level imagery, creating shape masks around unique fuzzy urban objects like trees. The novelty of this method is enhanced by using monocular depth estimation and triangulation to estimate precise tree location, relying only on photographs and images taken from the street. In combination with Google Street View, a technique for the rapid de- velopment of an extensive tree genera training dataset was presented based on the method of tree detection and location. This tree genera dataset was used to train a Convolutional Neural Network (CNN) for tree genera classification. Experiments across four cities show that the novel method for tree detection and location can be transferable to different image sources and urban ecosystems. Over 70% of trees recorded in a ground-truth campaign (2019) were detected and could be located with a mean error in the absolute position ranging from 4m to 6m, comparable to GPS accuracy used for geolocation in classical manual urban tree inventory campaigns. The trained CNN classifies 41 fine-grained tree genera classes with 83% accuracy. The detection and classification models were then used to generate maps of urban tree genera distribution in the Metro Vancouver region. Results of this research show that developed methods can be applied across different regions and cities and that deep learning and street-level imagery show promise to inform smart urban forest management, including bio-surveillance campaign planning.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".