Extracting Land Cover/Use from Remotely Sensed Imagery: Potentials for Urban Planning
Bibliographic record
Abstract
Remote sensing has a great potential for enhancing current urban planning processes by offering a holistic view of a study area and providing detailed land cover/use information within. This study intends to evaluate the potentials of remote sensing in urban/transportation planning by studying the accuracy of two image processing software (ENVI and Definiens) on extracting urban land cover/use. Several satellite images from the City of Fredericton, New Brunswick, Canada, including Landsat ETM+, SPOT4, IKONOS, and QuickBird, are used. It is found that medium-resolution images, Landsat ETM+and SPOT4, are only good at extracting large-size homogeneous objects, such as vegetation and water bodies, but less powerful for identifying small urban features, including buildings, streets and parking lots. Later experiments focus on extracting these urban features with very high-resolution imagery from IKONOS and QuickBird. Study results show that both software packages have more or less problems in distinguish parking lots, streets and building roofs because of similar materials used and therefore, very close spectral signatures. The results show the object-oriented hierarchical algorithm applied to QuickBird images offers the highest accuracy for building and street extractions, when compared to other algorithms/images combinations (such as maximum likelihood/nearest neighbor applied to QuickBird or IKONOS). The producer accuracy of the two is 78% and 63% respectively and the corresponding user accuracy is 39% and 56%. The study results clearly indicate that a more advanced approach using auxiliary data, such as GIS and LIDAR, is necessary to achieve an accuracy level acceptable for any real-world planning applications.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".