Estimated intercensal population for Germany (EstIP-DE) 2024

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Estimated intercensal population for Germany (EstIP-DE) 2024
Reiter, Denis, 2026, "Estimated intercensal population for Germany (EstIP-DE) 2024", https://doi.org/10.71830/IJWU6Q, ioerDATA, V1
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Description
EN: EstIP-DE offers small-scale population data for Germany, complementing official census data in between census surveys.

The population is estimated on building level by applying a tree-based AI model to 3D building footprints enhanced with a variety of geometrical, morphological and context-based features. The model is trained by dasymetrically mapping the most recent official census population (currently from 2022) to identified residential buildings and learns the relation between the processed building features and the mapped inhabitants.

The data is offered in two spatial resolutions:
1. 100m grid, using the same INSPIRE grid the official census uses. This version contains rounded and anonymised population values, using the same CKM method as the official census. Please refer to the official documentation for details on CKM (see "Related publications"). Inluded are only cells which have at least one building located in them. Cells can contain "0" values for population if no residential buildings are found in this cell; these values are not to be confused with "No data". This data is freely accessible to registered users.
2. Building level data. This version complements the building geometries of "3D Building Metrics Germany" and contains unaltered estimated population values for each building. This data is only available on request (see "Access and Usage Information").

The method for obtaining the population estimations is described in detail in the paper "Intercensal building-based population estimation". Evaluation revealed the error on grid cell level to be about 8%. Please refer to the paper for further details. Code for both obtaining the building features and the population estimations is documented on GitHub. Both the paper and code can be found under the "Related Publications" section. (2026-10-22)
DE: EstIP-DE bietet detaillierte Bevölkerungsdaten für Deutschland und ergänzt damit die offiziellen Zensusdaten zwischen den Erhebungen.

Die Bevölkerung wird auf Gebäudeebene geschätzt, indem ein baumbasiertes KI-Modell auf 3D-Gebäudegrundrisse angewendet wird, die mit einer Vielzahl geometrischer, morphologischer und kontextbezogener Gebäudemerkmale angereichert sind. Das Modell wird durch dasymetrische Verteilung der jüngsten offiziellen Zensusdaten (derzeit aus dem Jahr 2022) auf identifizierte Wohngebäude trainiert und lernt den Zusammenhang zwischen den Gebäudemerkmalen und den zugeordneten Einwohnern.

Die Daten werden in zwei räumlichen Auflösungen angeboten:
1. 100-m-Raster, unter Verwendung desselben INSPIRE-Rasters, das auch beim Zensus zum Einsatz kommt. Diese Version enthält gerundete und anonymisierte Bevölkerungswerte, wobei die Zensus-CKM-Methode zur Anonymisierung angewendet wird. Einzelheiten zur CKM-Methode finden Sie in der offiziellen Dokumentation (siehe "Zugehörige Publikationen"). Es sind nur Rasterzellen inkludiert, in denen sich mindestens ein Gebäude befindet. Zellen können den Wert „0“ für die Bevölkerungszahl enthalten, wenn in dieser Zelle keine Wohngebäude vorhanden sind; diese Werte sind nicht mit „Keine Daten“ zu verwechseln. Diese Daten sind für registrierte Benutzer frei zugänglich.
2. Daten auf Gebäudeebene. Diese Version ergänzt die Gebäudgeometrien von „3D Building Metrics Germany“ und enthält unveränderte geschätzte Bevölkerungszahlen für jedes Gebäude. Diese Daten sind nur auf Anfrage erhältlich (siehe "Zugangs- und Nutzungsinformationen").

Die Methode zur Ermittlung der Bevölkerungsschätzungen wird ausführlich in der Veröffentlichung „Intercensal building-based population estimation“ beschrieben. Eine Auswertung ergab, dass der Fehler auf Rasterzellenebene bei etwa 8 % liegt. Weitere Einzelheiten können dem Paper entnommen werden. Der Code sowohl zur Ermittlung der Gebäudemerkmale als auch zur Erstellung der Bevölkerungsschätzungen ist auf GitHub dokumentiert. Sowohl die Veröffentlichung als auch der Code sind im Abschnitt „Zugehörige Publikationen“ zu finden. (2026-10-22)
Subject Computer and Information Science; Engineering
Keyword residential building, population distribution, estimation, census
Related Publication Is Supplemented By: Reiter, D., Jehling, M., & Hecht, R. (2026). Intercensal building‑based population estimation. International Journal of Digital Earth, 19(2). https://doi.org/10.1080/17538947.2026.2738267doi: 10.1080/17538947.2026.2738267
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