  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.
