OBJECT-BASED LAND COVER CLASSIFICATION OF URBAN AREAS USING VHR IMAGERY AND PHOTOGRAMMETRICALLY-DERIVED DSM
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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