OBJECT-BASED LAND COVER CLASSIFICATION OF URBAN AREAS USING VHR IMAGERY AND PHOTOGRAMMETRICALLY-DERIVED DSM
Bibliographic record
Abstract
Object-based image analysis is becoming increasingl y popular in classification of very high resolution (VHR) imagery over urban areas. The spectral resolution o f VHR imagery (generally they possesses 1 pan and 4 multispectral bands), however, is limited and insuf ficient for differentiating many urban land cover c lasses. Due to the spectral similarity of building roofs, roads an d parking lots, spectral-based classifications whic h solely rely on spectral information of the image do not have promi sing results when applied to VHR imagery over urban landscapes. In recent years, significant amount of research has been carried out on incorporating LiDA R derived DSM into the classification to address the problems of differentiating spectrally similar objects in u rban areas. However, LiDAR DSMs are expensive and not available for many urban areas. In this research, we introdu ce a new approach for classifying urban land cover classes b y incorporating widely available photogrammetricall y-derived DSMs. Even though the accuracy of photogrammetrically-derived DSMs is far below that of LiDAR DSMs, and significant misregistration exists between VHR imagery and DSM, object- based hierarchical fuzzy class ification still achieve successful separation between buildin g roofs and traffic areas. Stereo aerial photos and a pansharped QuickBird multispectral image of the downtown area of the city of Fredericton, Canada, were used for t his research. Results show that buildings can be well separated f rom roads and parking lots, and the proposed approa ch has the potential to replace LiDAR DSM for urban land cover classification.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".