The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 379–386, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-379-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 379–386, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-379-2022
 
30 May 2022
30 May 2022

SAT2LOD2: A SOFTWARE FOR AUTOMATED LOD-2 BUILDING RECONSTRUCTION FROM SATELLITE-DERIVED ORTHOPHOTO AND DIGITAL SURFACE MODEL

S. Gui1,2, R. Qin1,2,3,4, and Y. Tang1,2 S. Gui et al.
  • 1Geospatial Data Analytics Laboratory, The Ohio State University, Columbus, USA
  • 2Department of Civil, Environmental and Geodetic Engineering, The Ohio State University, Columbus, USA
  • 3Department of Electrical and Computer Engineering, The Ohio State University, Columbus, USA
  • 4Translational Data Analytics Institute, The Ohio State University, Columbus, USA

Keywords: LoD-2 Building Reconstruction, Digital Surface Model, Orthophoto, Open-source software, Multi-stereo satellite images

Abstract. 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.