Multi-Scale Machine Learning for the Classification of Building Property Values

Patrick Helber, Benjamin Bischke, Jörn Hees, Andreas Dengel

In: Data Fusion: The AI Era. IEEE International Geoscience and Remote Sensing Symposium (IGARSS) July 28-August 2 Yokohama Japan IEEE 2019.


In this paper, we describe a multi-scale machine learning approach to estimate socio-economic attributes of citizens based on the analysis of aerial images. To analyse the effectiveness of the proposed approach we predict building property value classes. The classification of these building property values is a proxy for the socio-economic status of the residents. The approach is based on the fusion of deep Convolutional Neural Networks (CNNs). We compare the proposed approach with non-image and single-scale CNN approaches and demonstrate the effectiveness in a case study using statistical data collected in the city of Amsterdam, Netherlands. We show that the proposed multi-scale approach outperforms the baseline methods.


Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence