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 requires appropriate training data. OpenStreetMap (OSM) data is globaly available and partly provides information on building 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, residential 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 percent of the ground truth labels this shows the incomplete data can be used to train for the building classification task.

AIO2: Online Correction of Object Labels for Deep Learning With Incomplete Annotation in Remote Sensing Image Segmentation

“…The work of Q. Li and X. Zhu is jointly supported by the Excellence Strategy of the Federal Government and the Länder through the TUM Innovation Network EarthCare and by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab "AI4EO -Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond" (grant number: 01DD20001). The authors thank Nikolai Skuppin for sharing the Germany dataset [73] with them, and Nassim AIT ALI BRAHAM for discussions during the group meetings.…”

“…The Germany Dataset [73] consists of 2052 image-label pairs with a size of 320 × 320 pixels generated across ten Germany cities including Bielefeld, Bochum, Bonn, Cologne, Dortmund, Duesseldorf, Duisburg, Essen, Muenster, and Wuppertal. The image data were from Planet basemap images with a lower spatial resolution of 3m and 3 bands (RGB).…”