ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2, 177-184, 2018
https://doi.org/10.5194/isprs-annals-IV-2-177-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
 
28 May 2018
NEURAL NETWORKS FOR THE CLASSIFICATION OF BUILDING USE FROM STREET-VIEW IMAGERY
D. Laupheimer1, P. Tutzauer1, N. Haala1, and M. Spicker2 1Institute for Photogrammetry, University of Stuttgart, Germany
2Visual Computing Group, University of Konstanz, Germany
Keywords: Urban Data, Image Classification, Convolutional Neural Networks, Feature Learning, Class Activation Maps Abstract. Within this paper we propose an end-to-end approach for classifying terrestrial images of building facades into five different utility classes (commercial, hybrid, residential, specialUse, underConstruction) by using Convolutional Neural Networks (CNNs). For our examples we use images provided by Google Street View. These images are automatically linked to a coarse city model, including the outlines of the buildings as well as their respective use classes. By these means an extensive dataset is available for training and evaluation of our Deep Learning pipeline. The paper describes the implemented end-to-end approach for classifying street-level images of building facades and discusses our experiments with various CNNs. In addition to the classification results, so-called Class Activation Maps (CAMs) are evaluated. These maps give further insights into decisive facade parts that are learned as features during the training process. Furthermore, they can be used for the generation of abstract presentations which facilitate the comprehension of semantic image content. The abstract representations are a result of the stippling method, an importance-based image rendering.
Conference paper (PDF, 6753 KB)

Citation: Laupheimer, D., Tutzauer, P., Haala, N., and Spicker, M.: NEURAL NETWORKS FOR THE CLASSIFICATION OF BUILDING USE FROM STREET-VIEW IMAGERY, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2, 177-184, https://doi.org/10.5194/isprs-annals-IV-2-177-2018, 2018.

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