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Prediction of residential and non-residential building usage in Germany based on a novel nationwide reference data set

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  • André Hartmann
  • Martin Behnisch
  • Robert Hecht
  • Gotthard Meinel

Abstract

Building usage is an important variable in modelling the energetic, material and social properties of a building stock. Gathering this data on large geographical scale, and in the necessary temporal and spatial resolution, that means, on building level, is a challenging task. Machine Learning algorithms like Random Forest have proven useful in predicting building-related features in the past but often resort to training sets of limited geographic scope, for example, cities. This study presents a workflow of predicting the semantic attribute of usage on the level of individual buildings. Based on screening data of the previous ENOB:dataNWG project, a novel building ground-truth data set distributed across Germany, a Random Forest algorithm is used to assess how the German building stock can be classified according to its residential or non-residential use. Different sampling strategies had been applied in order to find a robust evaluation metric for the classifier. Furthermore, the relevance of the feature set is highlighted and it is examined whether regional differences in classification quality exist. Results show that a classification of residential and non-residential building footprints has good prospects with an AUC of up to 0.9.

Suggested Citation

  • André Hartmann & Martin Behnisch & Robert Hecht & Gotthard Meinel, 2024. "Prediction of residential and non-residential building usage in Germany based on a novel nationwide reference data set," Environment and Planning B, , vol. 51(1), pages 216-233, January.
    • Handle: RePEc:sae:envirb:v:51:y:2024:i:1:p:216-233
    DOI: 10.1177/23998083231175680
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    References listed on IDEAS

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