Integrating GEOBIA, Machine Learning, and Volunteered Geographic Information to Map Vegetation over Rooftops
Bibliographic record
Abstract
The objective of this study is to evaluate operational methods for creating a particular type of urban vegetation map—one focused on vegetation over rooftops (VOR), specifically trees that extend over urban residential buildings. A key constraint was the use of passive remote sensing data only. To achieve this, we (1) conduct a review of the urban remote sensing vegetation classification literature, and we then (2) discuss methods to derive a detailed map of VOR for a study area in Calgary, Alberta, Canada from a late season, high-resolution airborne orthomosaic based on an integration of Geographic Object-Based Image Analysis (GEOBIA), pre-classification filtering of image-objects using Volunteered Geographic Information (VGI), and a machine learning classifier. Pre-classification filtering lowered the computational burden of classification by reducing the number of input objects by 14%. Accuracy assessment results show that, despite the presence of senescing vegetation with low vegetation index values and deep shadows, classification using a small number of image-object spectral attributes as classification features (n = 9) had similar overall accuracy (88.5%) to a much more complex classification (91.8%) comprising a comprehensive set of spectral, texture, and spatial attributes as classification features (n = 86). This research provides an example of the very specific questions answerable about precise urban locations using a combination of high-resolution passive imagery and freely available VGI data. It highlights the benefits of pre-classification filtering and the judicious selection of features from image-object attributes to reduce processing load without sacrificing classification accuracy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".