Integration of Remote Sensing and GIS to Detect Pockets of Urban Poverty: The Case of Rosario, Argentina
Bibliographic record
Abstract
The advent of high spatial resolution, multispectral satellite imagery has allowed analysis of remotely sensed images of urban land cover to become more useful to urban planning and decision making than in the past. The addition of radar imagery at relatively high spatial resolution (6 metres at best), with the advantages that it is not affected by cloud and diurnal light conditions and that it is sensitive to the target's geometric shape, surface roughness and moisture content offers additional capability in this regard. This paper incorporates analysis of Canadian RADARSAT‐1 and American Landsat TM satellite imagery and ground‐based GIS data to identify known pockets of urban poverty. Poverty is defined, based on a limited number of census variables related to dwelling construction materials and per household overcrowding. The objective is to provide a proof of concept that remote sensing data, especially from synthetic aperture radar, and ground‐based GIS data can be successfully integrated for urban planning purposes. The results suggest that the approach used is reasonable and that, with future refinement, it offers planners and decision makers a timely and cost effective means to locate and monitor poverty pockets in urban areas. This is especially important in large, rapidly urbanising areas in the developing world.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".