Urban Structure Changes in Three Areas of Detroit, Michigan (2014–2018) Utilizing Geographic Object-Based Classification
Bibliographic record
Abstract
The following study utilized geographic object-based image analysis methods to detect pervious and impervious landcover with respect to residential structure changes. The datasets consist of freely available very high-resolution orthophotos acquired under the United States National Agriculture Imagery Program. Over the last several decades, cities in America’s Rust Belt region have experienced population and economic declines—most notably, the city of Detroit. With increased property vacancies, many residential structures are abandoned and left vulnerable to degradation. In many cases, one of the answers is to demolish the structure, leaving a physical, permanent change to the urban fabric. This study investigates the performance of object-based classification in segmenting and classifying orthophotos across three neighbourhoods (Crary/St. Mary, Core City, Pulaski) with different demolition rates within Detroit. The research successfully generated the distinction between pervious and impervious land cover and linked those to parcel lot administrative boundaries within the city of Detroit. Successful detection rates of residential parcels containing structures ranged from a low of 63.99% to a high of 92.64%. Overall, if there were more empty residential parcels, the detection method performed better. Pervious and impervious overall classification accuracy for the 2018 and 2014 imagery was 98.333% (kappa 0.966) with some slight variance in the producers and users statistics for each year.
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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 | 0.000 | 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".