Urban land use mapping using high resolution SAR data based on density analysis and contextual information
Bibliographic record
Abstract
This paper presents a procedure for urban land use interpretation from a single high-resolution synthetic aperture radar (SAR) image. The approach involves two semi-automatic steps: urban extent delineation and urban land use mapping. In the first step, two general classes (urban and nonurban) are mapped using an existing method that involves analysis of speckle characteristics and intensity information. In the second step, more detailed urban land use classification is undertaken based on analysis of regional radar backscatter patterns in terms of density of dark linear features, density of bright features, and urban contextual information. Density analysis was conducted at three levels: individual building–road, urban block, and suburban commercial–industrial. Contextual information, including density, building size, and distance between buildings and parking places, was used to quantify urban morphological patterns. Tests were conducted for mapping Ottawa, Canada, using five Radarsat-2 images of different incidence angles and three TerraSAR-X images of the same incidence angles but different dates. The results show that the proposed method could be used to map five urban land uses including low-density residential, commercial–industrial, high-density urban, open land, and nonurban with accuracies in the range from 74% to 82%.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".