Predicting residential building age from map data.

Abstract The age of a building influences its form and fabric composition and this in turn is critical to inferring its energy performance. However, often this data is unknown. In this paper, we present a methodology to automatically identify the construction period of houses, for the purpose of urban energy modelling and simulation. We describe two major stages to achieving this – a per-building classification model and post-classification analysis to improve the accuracy of the class inferences. In the first stage, we extract measures of the morphology and neighbourhood characteristics from readily available topographic mapping, a high-resolution Digital Surface Model and statistical boundary data. These measures are then used as features within a random forest classifier to infer an age category for each building. We evaluate various predictive model combinations based on scenarios of available data, evaluating these using 5-fold cross-validation to train and tune the classifier hyper-parameters based on a sample of city properties. A separate sample estimated the best performing cross-validated model as achieving 77% accuracy. In the second stage, we improve the inferred per-building age classification (for a spatially contiguous neighbourhood test sample) through aggregating prediction probabilities using different methods of spatial reasoning. We report on three methods for achieving this based on adjacency relations, near neighbour graph analysis and graph-cuts label optimisation. We show that post-processing can improve the accuracy by up to 8 percentage points. Highlights • Spatial datasets of building age are needed for energy modelling studies, but are not often available. • We demonstrate a data-driven approach to predict a per-building age classification and refine predictions based on spatial reasoning. • Map data, including building footprints, digital surface modelling and neighbourhood metrics serve as input predictors. • Spatial and topological relationships of buildings, including nearest neighbour analysis and graph-cuts based label optimisation are used to improve predictions. • Quantitative assessments of the accuracy for a UK case study. [ABSTRACT FROM AUTHOR]