Application of deep-learning techniques to very-high-resolution satellite images supporting population censuses in developing countries
Why this work is in the frame
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Bibliographic record
Abstract
Knowledge of demographic data is valuable information for planning initiatives. Typically, census, survey, and population projection exercises provide this information. In some developing countries, these operations pose a variety of economic and logistical challenges, thereby depriving authorities of accurate and timely information on their populations. To provide approaches for solving this situation, our study evaluates a population estimation method that is based on detection of residential geo-objects (houses) on very-high-resolution (VHR) satellite images using convolutional neural networks (CNN). The approach would be applicable to countries where a complete census is difficult to perform due to resource constraints or political instability. A 2008 VHR satellite image of Sudan is annotated according to seven classes of buildings to create a dataset that was used to train an object detection model, faster region-based CNN, by transfer learning. The model obtained mean average precision of 79% and 99% during training and validation, respectively. This unusual difference is due to the dominance of well detected classes in the validation dataset. The model was fine-tuned to detect the same building classes on images in 2021. A link between residential geo-objects and population size was established using 2008 population data and available field data. Subsequent characterization of the current population should assist in preparation of the 2023 census. Limitations of this approach were raised, but it could be used to improve the framework for population data collection in developing countries.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 it