In this paper, we examine potential applications of Neural Radiance Fields (NeRF) in the field of landscape architecture. NeRF is a state-of-the-art method for novel view synthesis and volu- metric scene reconstruction based on real-world training data. Our paper addresses NeRF and its de- rived models with a focus on the use and application of Instant-NGP, a method developed by research- ers from the technology company NVIDIA. We discuss experimental applications of NeRF based on the case study of the post-disaster landscape of Ahr Valley, Germany, affected by a 100-year flood in 2021. In particular, we are interested in the benefits of NeRF in comparison to other landscape modeling methods, such as Structure-from-Motion (SfM) or Multi-View-Stereo (MVS), which use similar data as input. This study shows that the application of NeRF technology can be a promising alternative for capturing and visualizing landscape scenes. The study focuses especially on tasks and situations where the larger spatial context – the landscape – is of interest and importance. The technological aspects of how NeRF models work are relevant, but our main focus is on their potential implications for the field of landscape architecture. Technical development and research in the scientific field of computer vision are acceler- ating rapidly. As users, rather than developers, of digital tools, we believe that NeRF technology re- quires professional validation through real-world landscape projects.
Schob et al. (Sat,) studied this question.