智能体感知建筑的卫星高斯泼溅用于可审计的城市DSM重建
Agentic Building-Aware Satellite Gaussian Splatting for Auditable Urban DSM Reconstruction
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中文总结 AI 辅助
提出一种智能体感知建筑的卫星高斯泼溅工作流，利用建筑掩膜语义先验和智能体重建控制器选择策略，在DFC2019场景中降低建筑区域DSM误差，实现可审计的城市三维重建。
中文摘要 AI 辅助
基于卫星影像的城市尺度三维重建支持灾害响应、城市监测和地理空间数字孪生，然而神经渲染方法通常优化平均视觉保真度，而非分析师首先检查的结构：建筑物。我们提出了一种智能体感知建筑的卫星高斯泼溅工作流，该工作流使用源自Segment Anything的建筑掩膜作为语义先验，并利用智能体重建控制器（Agentic Reconstruction Controller）来选择、验证和记录DSM重建策略。在DFC2019 JAX_004场景上，感知建筑的加权将建筑区域DSM平均绝对误差（MAE）从0.844米降低到0.806米，表明语义先验可以将重建能力转向分析师关键区域。分阶段调度提供了一个平衡的工作点，将全场景MAE从1.362米改善到1.349米，同时保留了建筑增益。在四个JAX场景中，智能体为通用DSM和建筑聚焦DSM目标选择经过验证的策略，并生成建筑清单元数据和每个场景的决策记录。该系统结合了语义先验、策略选择、区域特定DSM指标和源自DSM的GIS表面产品，用于可审计的城市三维分析。
英文摘要
Urban-scale 3D reconstruction from satellite imagery supports disaster response, city monitoring, and geospatial digital twins, yet neural rendering methods typically optimize average visual fidelity rather than the structures that analysts inspect first: buildings. We present an agentic building-aware satellite Gaussian Splatting workflow that uses Segment Anything-derived building masks as semantic priors and an Agentic Reconstruction Controller to select, verify, and record DSM reconstruction policies. On the DFC2019 JAX\_004 scene, building-aware weighting reduces building-region DSM MAE from 0.844 m to 0.806 m, showing that semantic priors can shift reconstruction capacity toward analyst-critical regions. A staged schedule provides a balanced operating point, improving full-scene MAE from 1.362 m to 1.349 m while retaining a building gain. Across four JAX scenes, the Agent selects validated policies for both general DSM and building-focused DSM objectives, and produces building-inventory metadata and per-scene decision records. The system combines semantic priors, policy selection, region-specific DSM metrics, and DSM-derived GIS surface products for auditable urban 3D analysis.
发表机构
- University of Waterloo（滑铁卢大学）
- University of Calgary（卡尔加里大学）
- Sun Yat-sen University（中山大学）
机构由 AI 辅助整理，请以论文原文为准。
