Predicting building types and functions at transnational scale

TL;DR

This study demonstrates that it is feasible to predict building types and functions across Europe using open GIS datasets and graph neural networks, achieving high accuracy and introducing novel methods for large-scale, multi-country building classification.

Contribution

The paper introduces a novel approach for large-scale building classification across multiple countries using GNNs and localized subgraphs, demonstrating improved accuracy over traditional methods.

Findings

GNN classifier achieves Cohen's kappa of 0.754 for 9 classes.

High accuracy in classifying residential vs. non-residential buildings with Cohen's kappa of 0.844.

Using localized subgraphs enhances GNN performance for building classification.

Abstract

Building-specific knowledge such as building type and function information is important for numerous energy applications. However, comprehensive datasets containing this information for individual households are missing in many regions of Europe. For the first time, we investigate whether it is feasible to predict building types and functional classes at a European scale based on only open GIS datasets available across countries. We train a graph neural network (GNN) classifier on a large-scale graph dataset consisting of OpenStreetMap (OSM) buildings across the EU, Norway, Switzerland, and the UK. To efficiently perform training using the large-scale graph, we utilize localized subgraphs. A graph transformer model achieves a high Cohen's kappa coefficient of 0.754 when classifying buildings into 9 classes, and a very high Cohen's kappa coefficient of 0.844 when classifying buildings into the residential and non-residential classes. The experimental results imply three core novel contributions to literature. Firstly, we show that building classification across multiple countries is possible using a multi-source dataset consisting of information about 2D building shape, land use, degree of urbanization, and countries as input, and OSM tags as ground truth. Secondly, our results indicate that GNN models that consider contextual information about building neighborhoods improve predictive performance compared to models that only consider individual buildings and ignore the neighborhood. Thirdly, we show that training with GNNs on localized subgraphs instead of standard GNNs improves performance for the task of building classification.