Bootstrapped CNNS for building segmentation on RGB-D aerial imagery

C. Sebastian, B. Boom, T. van Lankveld, E. Bondarev, P.H.N. de With

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)
98 Downloads (Pure)


Detection of buildings and other objects from aerial images has various applications in urban planning and map making. Automated building detection from aerial imagery is a challenging task, as it is prone to varying lighting conditions, shadows and occlusions. Convolutional Neural Networks (CNNs) are robust against some of these variations, although they fail to distinguish easy and difficult examples. We train a detection algorithm from RGB-D images to obtain a segmented mask by using the CNN architecture DenseNet. First, we improve the performance of the model by applying a statistical re-sampling technique called Bootstrapping and demonstrate that more informative examples are retained. Second, the proposed method outperforms the non-bootstrapped version by utilizing only one-sixth of the original training data and it obtains a precision-recall break-even of 95.10 % on our aerial imagery dataset.

Original languageEnglish
Pages (from-to)187-192
Number of pages6
JournalISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Issue number4
Publication statusPublished - 19 Sept 2018
Event2018 ISPRS TC IV Mid-Term Symposium on 3D Spatial Information Science - The Engine of Change - Delft, Netherlands
Duration: 1 Oct 20185 Oct 2018


  • aerial imagery
  • bootstrapping
  • building segmentation
  • deep learning


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