Representation Learing method for Full waveform LiDAR data

Published:

  • This paper presents a novel representation learning method for spatially distributed full-waveform data observed from an ALS using an AE-based architecture called FWNetAE.

  • The results demonstrate a generalization error for invisible test data.
  • Moreover, the FWNetAE encoded a meaningful latent vector and the decoders reconstructed the spatial geometry and its waveform value from the encoded latent vector.