Learning Neural Radiance Fields of Forest Structure for Scalable and
  Fine Monitoring
Juan Castorena
This work leverages neural radiance fields and remote sensing for forestry
applications. Here, we show neural radiance fields offer a wide range of
possibilities to improve upon existing remote sensing methods in forest
monitoring. We present experiments that demonstrate their potential to: (1)
express fine features of forest 3D structure, (2) fuse available remote sensing
modalities and (3), improve upon 3D structure derived forest metrics.
Altogether, these properties make neural fields an attractive computational
tool with great potential to further advance the scalability and accuracy of
forest monitoring programs.