---
title: "LTM: Large-scale Terrain Model for Wildfire-prone Landscapes"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.08711v1"
permalink: "https://feed.astralor.com/items/itm_6fb27610474373f7cde17637"
published: "2026-07-09T17:22:35.000Z"
score: 77
channel: "ai"
tags: ["LTM","Multi-modal Reconstruction","Digital Elevation Model (DEM)","Computer Vision","Wildfire Disaster"]
---

# LTM: Large-scale Terrain Model for Wildfire-prone Landscapes

## AI commentary

> This paper cleverly uses outdated DEMs as priors to address sparse features in image-based reconstruction without expensive LiDAR. It is directly relevant for researchers and practitioners in geospatial AI and emergency response, especially fire departments seeking real-time terrain data at low cost.

## At a glance

Accurate 3D terrain maps are critical for wildfire emergency response, but conventional methods are costly or inaccurate. This paper proposes LTM, a multi-modal reconstruction framework that uses outdated Digital Elevation Models (DEMs) as geometric priors. With physics-based pixel-pixel alignment, it fuses images with DEM data without expensive feature matching, producing real-time high-fidelity depth maps on a large-terrain simulator based on a real wildfire area, significantly improving accuracy and efficiency.

## Key points

- Proposes LTM framework that fuses outdated DEMs as geometric priors with images for 3D reconstruction.
- Innovation: physics-based pixel-level alignment avoids feature matching, reducing computational complexity.
- Validated on a large-terrain simulator based on a real wildfire area, producing real-time high-fidelity depth maps.
- Significant improvements in reconstruction accuracy and computational efficiency over existing image-based methods.

## Full text

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 We propose a multi-modal reconstruction framework leveraging outdated Digital Elevation Models (DEMs) as geometric priors for image-based 3D reconstruction.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.

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