Semi-Automatic Building Extraction Utilizing QuickBird Imagery
Bibliographic record
Abstract
Extraction of geospatial data from digital images is one of the most complex and challenging tasks faced by computer vision and photogrammetry communities. Geospatial data and buildings in particular are required for varieties of applications such as urban planning, creation of GIS databases, and urban city models. For decades, extraction of geospatial data in urban areas has been by conventional photogrammetry using aerial photos. This method is expensive, manually operated and require well-trained personnel. In recent years we have experienced the emergence of high-resolution space borne images, which have disclosed a large number of new opportunities for medium and large-scale topographic mapping. In this paper, we have developed a semi-automatic method to extract buildings in structured and unstructured urban settlements areas from high-spatial resolution panchromatic imagery. The proposed method uses radial casting algorithm to initialize snakes contours, and the fine measurements of building outlines is carried out using snakes model. The results are satisfactory with an extraction rate of 91 percent as demonstrated by examples over a variety of selected test areas. The potential, limitations and future work of our approach is discussed.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".