Gridding Forced Displacement using Semi-Supervised Learning
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912422132973568 |
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| author | Wells, Andrew Henningsen, Geraldine Kengne, Brice Bolane Tchinde |
| author_facet | Wells, Andrew Henningsen, Geraldine Kengne, Brice Bolane Tchinde |
| contents | We present a semi-supervised approach that disaggregates refugee statistics from administrative boundaries to 0.5-degree grid cells across 25 Sub-Saharan African countries. By integrating UNHCR's ProGres registration data with satellite-derived building footprints from Google Open Buildings and location coordinates from OpenStreetMap Populated Places, our label spreading algorithm creates spatially explicit refugee statistics at high granularity.This methodology achieves 92.9% average accuracy in placing over 10 million refugee observations into appropriate grid cells, enabling the identification of localized displacement patterns previously obscured in broader regional and national statistics. The resulting high-resolution dataset provides a foundation for a deeper understanding of displacement drivers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_08019 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Gridding Forced Displacement using Semi-Supervised Learning Wells, Andrew Henningsen, Geraldine Kengne, Brice Bolane Tchinde Machine Learning Computer Vision and Pattern Recognition Computers and Society We present a semi-supervised approach that disaggregates refugee statistics from administrative boundaries to 0.5-degree grid cells across 25 Sub-Saharan African countries. By integrating UNHCR's ProGres registration data with satellite-derived building footprints from Google Open Buildings and location coordinates from OpenStreetMap Populated Places, our label spreading algorithm creates spatially explicit refugee statistics at high granularity.This methodology achieves 92.9% average accuracy in placing over 10 million refugee observations into appropriate grid cells, enabling the identification of localized displacement patterns previously obscured in broader regional and national statistics. The resulting high-resolution dataset provides a foundation for a deeper understanding of displacement drivers. |
| title | Gridding Forced Displacement using Semi-Supervised Learning |
| topic | Machine Learning Computer Vision and Pattern Recognition Computers and Society |
| url | https://arxiv.org/abs/2506.08019 |