Learning Neural Radiance Fields of Forest Structure for Scalable and Fine Monitoring
This work addresses forest monitoring for environmental management, but it is incremental as it adapts existing neural radiance fields to a new domain.
The paper tackles the problem of forest monitoring by applying neural radiance fields to remote sensing, showing they can express fine 3D forest features, fuse modalities, and improve forest metrics, enhancing scalability and accuracy.
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.