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

André Hartmann, Martin Behnisch, Robert Hecht and Gotthard Meinel

Environment and Planning B, 2024, vol. 51, issue 1, 216-233

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.

Keywords: building stock; building usage; classification; machine learning; Random Forest classifier; feature importance; spatial cross-validation (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:sae:envirb:v:51:y:2024:i:1:p:216-233

DOI: 10.1177/23998083231175680

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