@article{MuellerLeppichGeissetal.2023,
  author    = {M{\"u}ller, Konstantin and Leppich, Robert and Geiß, Christian and Borst, Vanessa and Pelizari, Patrick Aravena and Kounev, Samuel and Taubenb{\"o}ck, Hannes},
  title     = {Deep neural network regression for normalized digital surface model generation with Sentinel-2 imagery},
  series = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  volume    = {16},
  journal   = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  issn      = {1939-1404},
  doi       = {10.1109/JSTARS.2023.3297710},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-349424},
  pages     = {8508-8519},
  year      = {2023},
  abstract  = {In recent history, normalized digital surface models (nDSMs) have been constantly gaining importance as a means to solve large-scale geographic problems. High-resolution surface models are precious, as they can provide detailed information for a specific area. However, measurements with a high resolution are time consuming and costly. Only a few approaches exist to create high-resolution nDSMs for extensive areas. This article explores approaches to extract high-resolution nDSMs from low-resolution Sentinel-2 data, allowing us to derive large-scale models. We thereby utilize the advantages of Sentinel 2 being open access, having global coverage, and providing steady updates through a high repetition rate. Several deep learning models are trained to overcome the gap in producing high-resolution surface maps from low-resolution input data. With U-Net as a base architecture, we extend the capabilities of our model by integrating tailored multiscale encoders with differently sized kernels in the convolution as well as conformed self-attention inside the skip connection gates. Using pixelwise regression, our U-Net base models can achieve a mean height error of approximately 2 m. Moreover, through our enhancements to the model architecture, we reduce the model error by more than 7\%.},
  language  = {en}
}