Preliminary Study of Urban Land Use Classification Using Historical Aerial Photos and AI Technology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Historical aerial photography is potentially an important source for historical urban development studies, uncovering new social and environment histories. However, vast and underutilized historical aerial photo archives have been underutilized in such research. Here we present a preliminary study that uses historical aerial photos for urban land use classification, using AI technologies. We explore the potential of improving the efficiency historical aerial photo use for urban research. The Deep Learning algorithm, U-Net, is used in this study for urban land use classification of 16 categories/classes. Although it is more difficult to classify historical aerial photos with only three spectral bands than classifying multispectral images with four or more spectral bands, we still achieved an overall accuracy of 82%. Potentials for further improvements are also identified.
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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 | 0.001 |
| 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 it