Enregistré dans:
Détails bibliographiques
Auteurs principaux: Gerrits, Petrus J., Erünal, Efe, Kabadayi, M. Erdem, Basiri, Ana, Sertel, Elif
Format: Preprint
Publié: 2025
Sujets:
Accès en ligne:https://arxiv.org/abs/2512.12527
Tags: Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
_version_ 1866914200677253120
author Gerrits, Petrus J.
Erünal, Efe
Kabadayi, M. Erdem
Basiri, Ana
Sertel, Elif
author_facet Gerrits, Petrus J.
Erünal, Efe
Kabadayi, M. Erdem
Basiri, Ana
Sertel, Elif
contents Accurate reconstruction of historical population distributions from the 1970s to the 1990s remains a significant limitation in global gridded population products due to coarse built-up data and limited census records. This study is, to our knowledge, the first to integrate declassified Hexagon KH-9 reconnaissance imagery into gridded population mapping. We enhance the GHS-POP framework by combining segmented built-up land cover from the HexaLCSeg dataset, derived from 1977 KH-9 imagery, with geocoded settlement-level census data to construct high-resolution historical population grids. Applied to Arnavutkoy and Cekmekoy in Istanbul for the period 1975-1990, we evaluate three dasymetric approaches, including a standard GHSL baseline, a Hexagon-enhanced workflow, and a fully integrated model incorporating local census records. Pixel-wise and zonal analyses show that GHSL misallocates populations to historically undeveloped regions, while the Hexagon-derived dataset substantially improves the representation of fragmented rural and peri-urban areas often missing from global products. Incorporating settlement-level LAU-2 census data further refines spatial population distribution. The results demonstrate that combining historical reconnaissance imagery with high-resolution census data improves the accuracy of historical population grids, and given the global coverage of declassified missions, this methodology offers significant potential for reconstructing historical population patterns in data-scarce regions worldwide.
format Preprint
id arxiv_https___arxiv_org_abs_2512_12527
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Enhancing GHSL Population Grids Using Hexagon KH-9 Built-up Data: Refining 1970s Rural and Peri-Urban Distributions in Istanbul
Gerrits, Petrus J.
Erünal, Efe
Kabadayi, M. Erdem
Basiri, Ana
Sertel, Elif
Physics and Society
Accurate reconstruction of historical population distributions from the 1970s to the 1990s remains a significant limitation in global gridded population products due to coarse built-up data and limited census records. This study is, to our knowledge, the first to integrate declassified Hexagon KH-9 reconnaissance imagery into gridded population mapping. We enhance the GHS-POP framework by combining segmented built-up land cover from the HexaLCSeg dataset, derived from 1977 KH-9 imagery, with geocoded settlement-level census data to construct high-resolution historical population grids. Applied to Arnavutkoy and Cekmekoy in Istanbul for the period 1975-1990, we evaluate three dasymetric approaches, including a standard GHSL baseline, a Hexagon-enhanced workflow, and a fully integrated model incorporating local census records. Pixel-wise and zonal analyses show that GHSL misallocates populations to historically undeveloped regions, while the Hexagon-derived dataset substantially improves the representation of fragmented rural and peri-urban areas often missing from global products. Incorporating settlement-level LAU-2 census data further refines spatial population distribution. The results demonstrate that combining historical reconnaissance imagery with high-resolution census data improves the accuracy of historical population grids, and given the global coverage of declassified missions, this methodology offers significant potential for reconstructing historical population patterns in data-scarce regions worldwide.
title Enhancing GHSL Population Grids Using Hexagon KH-9 Built-up Data: Refining 1970s Rural and Peri-Urban Distributions in Istanbul
topic Physics and Society
url https://arxiv.org/abs/2512.12527