Object-based change detection (OBCD): a case study for measuring retail led regeneration
Bibliographic record
Abstract
The Canadian retail landscape experienced a defining shift in the 1990s with the emergence of “big box” retailers and the proliferation of power centers nationwide. Within this context, the Ontario Stockyards neighborhood in Toronto, Canada, has undergone a remarkable transformation over the past three decades. It has evolved from a predominantly meat-packing industrial hub into a versatile urban environment featuring a mix of retail and residential spaces. This research seeks to elucidate the dynamics of commercial/industrial and residential area changes within this evolving landscape. The study spans a 23-year period, during which the distribution of land use categories undergoes notable fluctuations. This study sheds light on the intricate relationship between retail-driven urban regeneration, commercial/industrial transformations, and residential development. This paper uses the Stockyards neighborhood as a case study, to demonstrate the practical application of Object-Based Change Detection (OBCD) in real-world contexts. Through this case study, the potential and effectiveness of OBCD as a valuable tool for analyzing complex urban development is presented. The findings underscore the significance of integrated urban development strategies that leverage the transformative potential of retail-led initiatives.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".