用单张影像生成厘米级地形图，精度逼近激光雷达。
Seamless High-Resolution Terrain Reconstruction: A Prior-Based Vision Transformer Approach
- 基于先验的视觉变换器，融合低分辨率地图与高分辨影像重建地形。
- 实现30米到30厘米的100倍分辨率提升，误差低于5米。
- 适合地理信息、灾害评估与环境监测领域的研究者使用。
高分辨率高程数据对水文建模、灾害评估和环境监测至关重要，但全球一致的精细数字高程模型（DEMs）仍不可得。本研究利用单视角高分辨率影像，在像素级别提取地形信息，实现了大范围精细地形重建。提出一种基于先验的单目深度基础模型（MDE），将其扩展至遥感高程领域，通过结合低分辨率SRTM数据作为全局先验，并融合来自国家农业影像计划（NAIP）的高分辨率RGB影像，生成接近激光雷达精度的高分辨率DEM。该方法实现从30米到30厘米的100倍分辨率提升，优于现有超分辨率方法一个数量级。在两种不同地貌中，模型表现稳健，平均绝对误差低于5米，相较SRTM提升最高达18%。流域与坡面尺度的水文分析验证了其在灾害评估与环境监测中的实用性，显著改善了径流模拟与流域划分。最后，框架可扩展至大区域应用。
原文摘要 · Abstract (English)
High-resolution elevation data is essential for hydrological modeling, hazard assessment, and environmental monitoring; however, globally consistent, fine-scale Digital Elevation Models (DEMs) remain unavailable. Very high-resolution single-view imagery enables the extraction of topographic information at the pixel level, allowing the reconstruction of fine terrain details over large spatial extents. In this paper, we present single-view-based DEM reconstruction shown to support practical analysis in GIS environments across multiple sub-national jurisdictions. Specifically, we produce high-resolution DEMs for large-scale basins, representing a substantial improvement over the 30 m resolution of globally available Shuttle Radar Topography Mission (SRTM) data. The DEMs are generated using a prior-based monocular depth foundation (MDE) model, extended in this work to the remote sensing height domain for high-resolution, globally consistent elevation reconstruction. We fine-tune the model by integrating low-resolution SRTM data as a global prior with high-resolution RGB imagery from the National Agriculture Imagery Program (NAIP), producing DEMs with near LiDAR-level accuracy. Our method achieves a 100x resolution enhancement (from 30 m to 30 cm), exceeding existing super-resolution approaches by an order of magnitude. Across two diverse landscapes, the model generalizes robustly, resolving fine-scale terrain features with a mean absolute error of less than 5 m relative to LiDAR and improving upon SRTM by up to 18 %. Hydrological analyses at both catchment and hillslope scales confirm the method's utility for hazard assessment and environmental monitoring, demonstrating improved streamflow representation and catchment delineation. Finally, we demonstrate the scalability of the framework by applying it across large geographic regions.
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