Enhancing Building Semantic Segmentation Accuracy with Super Resolution
  and Deep Learning: Investigating the Impact of Spatial Resolution on Various
  Datasets
Zhiling Guo Xiaodan Shi Haoran Zhang Dou Huang Xiaoya Song Jinyue Yan Ryosuke Shibasaki
The development of remote sensing and deep learning techniques has enabled
building semantic segmentation with high accuracy and efficiency. Despite their
success in different tasks, the discussions on the impact of spatial resolution
on deep learning based building semantic segmentation are quite inadequate,
which makes choosing a higher cost-effective data source a big challenge. To
address the issue mentioned above, in this study, we create remote sensing
images among three study areas into multiple spatial resolutions by
super-resolution and down-sampling. After that, two representative deep
learning architectures: UNet and FPN, are selected for model training and
testing. The experimental results obtained from three cities with two deep
learning models indicate that the spatial resolution greatly influences
building segmentation results, and with a better cost-effectiveness around
0.3m, which we believe will be an important insight for data selection and
preparation.