论文标题
SAT2LOD2：一种用于自动化LOD-2建模的软件，来自卫星衍生的正赶和数字表面模型
Sat2lod2: A Software For Automated Lod-2 Modeling From Satellite-Derived Orthophoto And Digital Surface Model
论文作者
论文摘要
从卫星图像重建的正射击和数字表面模型（DSM）中得出LOD2模型是一项具有挑战性的任务。现有解决方案主要是系统方法，需要复杂的逐步过程，不仅包括启发式几何操作，还需要高级步骤，例如基于机器的语义细分和建筑物检测。在本文中，我们描述了一种名为SAT2LOD2的开源工具，该工具基于我们最近发布的作品的次要修改版本构建。 SAT2LOD2是基于Python的完全开源和GUI（图形用户界面）的软件，该软件以Python进行编码，该软件将正赶动物和DSM作为输入，并输出单个构建模型，并且还可以使用Road Network网络ShapeFiles，并定制的分类映射以进一步改善重建结果。我们通过1）将基于HRNETV2的建筑物分割成我们软件进一步提高了该方法的鲁棒性； 2）实施了一种决策策略来识别复杂的建筑物并直接生成网格，以避免从系统的角度进行错误的LOD2重建。该软件可以使用支持CUDA的图形卡的PC处理中等水平的数据（约5000*5000尺寸的正射和DSM）。此外，GUI是独立的，并存储中间处理结果，促进研究人员轻松学习该过程，并根据需要重复使用中间文件。更新的代码和软件可在此GitHub页面下找到：https：//github.com/gdaosu/lod2buildingmodel。
Deriving LoD2 models from orthophoto and digital surface models (DSM) reconstructed from satellite images is a challenging task. Existing solutions are mostly system approaches that require complicated step-wise processes, including not only heuristic geometric operations, but also high-level steps such as machine learning-based semantic segmentation and building detection. Here in this paper, we describe an open-source tool, called SAT2LOD2, built based on a minorly modified version of our recently published work. SAT2LoD2 is a fully open-source and GUI (Graphics User Interface) based software, coded in Python, which takes an orthophoto and DSM as inputs, and outputs individual building models, and it can additionally take road network shapefiles, and customized classification maps to further improve the reconstruction results. We further improve the robustness of the method by 1) intergrading building segmentation based on HRNetV2 into our software; and 2) having implemented a decision strategy to identify complex buildings and directly generate mesh to avoid erroneous LoD2 reconstruction from a system point of view. The software can process a moderate level of data (around 5000*5000 size of orthophoto and DSM) using a PC with a graphics card supporting CUDA. Furthermore, the GUI is self-contained and stores the intermediate processing results facilitating researchers to learn the process easily and reuse intermediate files as needed. The updated codes and software are available under this GitHub page: https://github.com/GDAOSU/LOD2BuildingModel.
