This repository contains the Python codes generated from the ISPRS Scientific Initiatives project Integrating IndoorGML with outdoors: Automatic routing graph generation for indoor‐outdoor transitional space for seamless navigation. In this scientific initiative, we focus on modeling of indoor-outdoor transitional space, and aim to develop a tool which can automatically construct navigation graphs for indoor-outdoor transitional space to seamlessly link the outdoor environments and indoor networks.

If you use any of our codes or data for your research, please cite the following papers:

Wang, Z., Zlatanova, S., Mostafavi, M. A., Khoshelham, K., Díaz-Vilariño, L., & Li, K. J. (2023). Automatic Generation of Routing Graphs for Indoor-Outdoor Transitional Space to Support Seamless Navigation. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 10, 487-492.

The main Python file is

VoxelModelBuilder.py

You need to specify the txt file, which contains the point cloud data in the following format (lng, lat, height, label). The output contains two files: 1) a gltf/glb file (which can be visualized with https://github.khronos.org/glTF-Sample-Viewer-Release/); 2) an indoorgml file (which can be visualized with IndoorGML viewer https://github.com/STEMLab/InViewer-Desktop).

There are three point cloud datasets that have been used for this project: 1) Cheltenham Spa railway station in Victoria (Australia); 2) Research Center for Telecommunication Technologies in the University of Vigo (Spain); 3) The building of the School of Mining and Energy Engineering in the University of Vigo (Spain). The point clouds cover cover not only the indoor environments but also the indoor-outdoor transitional space that connects the roads and the entrances.

data	Voxel model	IndoorGML model
data1
data2
data3