Object-Oriented Classification for Change Detection with Different Spatial Resolution Images
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
Aerial photos have been increasingly and commonly used in various spatially related applications. Many municipalities and government agencies have constructed aerial photo databases all over the world. Keeping these databases up to date is the most important part of making them effective so that aerial photo databases are expected to be updated as frequently as possible. However, in practice, some of them are barely updated because of high cost. In this study, medium spatial resolution imagery is proposed to detect changes. Instead of using aerial photos, free accessible Landsat ETM+ from GeoBase and orthophotomaps from SODB are used for change detection. In order to compare with different spatial resolution images orthophotomaps are decomposed, segmented, and classified through wavelet transform and object-oriented classification. Although the detected changes are rough, the result shows that the method is quite cost-effective and practical. Moreover, it could support decision making for updating aerial photo databases.
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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 | 0.000 |
| 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 it