Change detection of buildings in urban environment from high spatial resolution satellite images using existing cartographic data and prior knowledge
Bibliographic record
Abstract
Several studies in remote sensing image processing have tackled the issue of change detection for cartographic needs. Many algorithms have been developed, but very few have been applied for urban studies to update maps from high-spatial-resolution remote sensing images. The semantic richness of the image increases and makes image analysis more difficult. Change detection from high spatial resolution images such as Ikonos and QuickBird is even more challenging, especially in complex environments like urban areas characterized by small objects such as houses, individual trees and roads, and by shadows. In addition, even if they are usually available, existing digital map data often are not incorporated in all steps of change detection process. This research project proposes a new object oriented method for the detection of building changes from high-spatial-resolution images in urban areas inspired by the specific problem of guiding the process by using existing cartographic data and knowledge. The use of the existing digital map and the integration of existing knowledge allow optimizing the change detection process on the image while offering the possibility to target and to accelerate the research of the changes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".