Building Stock and Building Typology of Kigali, Rwanda
Bibliographic record
Abstract
Dynamically changing urban agglomerations in emerging countries in the Global South experience rapid changes in their urban extent and morphology, due to a growth of population and migration, as well as socioeconomic developments. It is important to have access to updated information on the qualitative and quantitative status of settlements, in order to monitor and inform the housing sector, spatial and infrastructure planning, municipal revenue collection and budgeting, and the supply of social services. Very high-resolution (VHR) multispectral satellite images are one important, cost effective, source of regular, updated, data on urban land-use and built-up areas. The authors acquired a Pléiades satellite image from August 2015 for the central part of the capital of Rwanda, Kigali. Object-based image analysis (OBIA) and expert-based post-classification were then applied to derive building footprints and building heights, and to assign all buildings to nine building archetypes. In a second step, building footprint data from aerial images of the same area in 2008-2009 were analysed, to identify the change of the building stock in the respective period. In total, 165,625 built entities have been detected for 2008-2009 and 211,458 for 2015; this entails a 27.7% increase in the number of buildings. The dataset presented is a completely revised version of a dataset that was used for a published report on the housing supply in Kigali in 2018.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".