LTM: Large-scale Terrain Model for Wildfire-prone Landscapes
This work addresses the need for accurate, low-cost 3D terrain maps in wildfire-prone landscapes, enabling faster emergency response.
The paper introduces a multi-modal reconstruction framework that uses outdated Digital Elevation Models (DEMs) as geometric priors for image-based 3D terrain reconstruction, achieving high-fidelity depth maps with real-time performance and significant improvements in accuracy and efficiency over existing methods.
Accurate 3D terrain maps are essential for emergency response when assessing wildfire hazards. However, wildfire-prone regions often span vast areas where conventional reconstruction methods underperform. Airborne LiDAR systems provide high-resolution terrain data, but they are expensive and infrequently updated. Image-based methods offer a lower-cost alternative, but struggle due to sparse visual features and limited image overlap. We propose a multi-modal reconstruction framework leveraging outdated Digital Elevation Models (DEMs) as geometric priors for image-based 3D reconstruction. Our key innovation is physics-based pixel-pixel alignment between images and DEM data, dramatically reducing computational complexity by eliminating expensive feature matching procedures. To validate our approach, we developed a large-terrain simulator based on a real wildfire-prone area, generating realistic images enabling a comprehensive evaluation. Given posed images and legacy DEMs, our method produces high-fidelity depth maps while maintaining real-time performance. We find significant improvements in reconstruction accuracy and computational efficiency over existing techniques, offering a scalable solution for wildfire response.