Analysis of Land Use Extraction through Morphological Analysis Using Geographic and Remotely Sensed Data
Bibliographic record
Abstract
Lack of detailed land use (LU) information and inefficient data gathering methods have made modeling of urban systems difficult. This study aims to develop a hybrid remote sensing (RS)/geographic information (GI) system in order to extract residential LU information from very high resolution (VHR), remotely sensed imagery. Land cover information extracted from remote sensing and several types of geographic data from the study area (City of Fredericton, Canada) are fused into the residential LU extraction expert system to examine correlation/association rules at the building level. Morphological analysis at the building level is used through a step-wise binary logistic regression model to provide a set of multi-dimensional indicators for extracting the residential buildings. In this regard, sets of morphological properties derived from geographic vector and remotely sensed data are used in a binary regression model. LU classification from the morphological analysis results in an overall accuracy of 93.2% for extracting residential buildings. It should be noted that equipped with such a powerful LU data collection tool and detailed LU data, urban planners/modellers can more reliably and precisely predict economic interactions, activity locations, space and housing developments, business expansion, and trip patterns.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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 | 0.001 |
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".