  Building detection from satellite multispectral imagery data is being a
fundamental but a challenging problem mainly because it requires correct
recovery of building footprints from high-resolution images. In this work, we
propose a deep learning approach for building detection by applying numerous
enhancements throughout the process. Initial dataset is preprocessed by 2-sigma
percentile normalization. Then data preparation includes ensemble modelling
where 3 models were created while incorporating OpenStreetMap data. Binary
Distance Transformation (BDT) is used for improving data labeling process and
the U-Net (Convolutional Networks for Biomedical Image Segmentation) is
modified by adding batch normalization wrappers. Afterwards, it is explained
how each component of our approach is correlated with the final detection
accuracy. Finally, we compare our results with winning solutions of SpaceNet 2
competition for real satellite multispectral images of Vegas, Paris, Shanghai
and Khartoum, demonstrating the importance of our solution for achieving higher
building detection accuracy.
