Inventory of Slums with Remote Sensing Methodology as A Step to Educate A Sustainable City (Case Study of Mapping Slums in The City of Bandung)
Bibliographic record
Abstract
Urban slums remain a significant challenge in Indonesia, exacerbated by rapid population growth and inadequate local government intervention. Remote sensing technologies offer high accuracy in mapping these areas, yet a lack of community engagement and knowledge hinders their effective application. This study aims to explore the integration of remote sensing applications with community engagement strategies to enhance urban planning and development in densely populated slum areas. The research employs case studies across various urban areas in Indonesia, complemented by participatory workshops with community members and local officials to gather insights and develop a participatory framework. The study identifies barriers to stakeholder engagement and highlights the potential of combining remote sensing data with local knowledge to create actionable urban development plans. The findings contribute to sustainable urban development discourse by providing guidelines for local governments on leveraging remote sensing data while actively involving communities, ultimately improving urban planning outcomes in Indonesia.
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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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".