Exploring the Application of NeRF in Enhancing Post-Disaster Response
A Case Study of the Sasebo Landslide in Japan
Bibliographic Data
| ID | 22033232 |
|---|---|
| Authors | Jinge Zhang (0009-0000-2914-8189, Nanyang Technological University), Yan Du (0000-0002-1790-5303, Space Engineering University), Yujing Jiang (0000-0002-4020-5989, Nagasaki University, corresponding author), Sunhao Zhang (Nanyang Technological University), Hongbin Chen (0000-0003-4008-3704, Nagasaki University), Dongqi Shang (0009-0009-6551-6810, Nagasaki University) |
| Year | 2025 |
| Volume | 14 |
| Issue | 6 |
| Pages | 218 |
| Publication date | 2025-05-30 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi14060218 |
| OpenAlex | W4410967828 |
| Language | EN |
| Citations received | 1 |
| References cited | 44 |
Rapid acquisition of 3D reconstruction models of landslides is crucial for post-disaster emergency response and rescue operations. This study explores the application potential of Neural Radiance Fields (NeRF) technology for rapid post-disaster site modeling and performs a comparative analysis with traditional photogrammetry methods. Taking a landslide induced by heavy rainfall in Sasebo City, Japan, as a case study, this research utilizes drone-acquired video imagery data and employs two different 3D reconstruction techniques to create digital models of the landslide area. Visual realism and point cloud detail were compared. The results indicate that the high-capacity NeRF model (NeRF 24G) approaches or even surpasses traditional photogrammetry in visual realism under certain scenarios; however, the generated point clouds are inferior in terms of detail compared to those produced by traditional photogrammetry. Nevertheless, NeRF significantly reduces the modeling time. NeRF 6G can generate a point cloud of engineering-useful accuracy in only 45 min, providing a 3D overview of the disaster site to support emergency response efforts. In the future, integrating the advantages of both methods could enable rapid and precise post-disaster 3D reconstruction
Computer vision · Construction engineering · Disaster area · Geotechnical engineering · Landslide · Photogrammetry · Point cloud · Remote sensing · 3D Surveying and Cultural Heritage · Computer Science · Engineering · Landslides and related hazards · Remote Sensing and LiDAR Applications · Artificial Intelligence · Geology
| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2025 - 2025 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 1 |