Building type classification with incomplete labels

Abstract

Buildings can be distinguished by their form or function and maps of building types can be used by authorities for city planning. Training models to perform this classification re- quires appropriate training data. OpenStreetMap (OSM) data is globaly available and partly provides information on build- ing types. However, this data can be incomplete or wrong. In this work a U-Net is trained to group buildings into one of the three major function classes (commercial/industrial, residen- tial and other) using incomplete OSM data or ground-truth cadastral data. The model achieves overall accuracies of 72 and 75 percent. Given the OSM data has only around 20 per- cent of the ground truth labels this shows the incomplete data can be used to train for the building classification task.