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Automatic 3D Building Reconstruction from OpenStreetMap and LiDAR Using Convolutional Neural Networks.

Marcos Barranquero¹, Alvaro Olmedo¹, Josefa Gómez¹

¹Computer Science Department, Universidad de Alcalá, 28801 Alcalá de Henares, Spain.

Sensors (Basel, Switzerland)

|March 11, 2023

Summary

This study introduces an automatic method combining OpenStreetMap and LiDAR data to create detailed 3D building maps. The approach accurately infers missing building height and roof data, enhancing 3D urban models.

Keywords:

3D reconstruction LiDAR OpenStreetMap convolutional neural network machine learning transfer learning

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Area of Science:

Geographic Information Systems (GIS)
Computer Vision
Remote Sensing

Background:

OpenStreetMap (OSM) data lacks detailed building information like height and roof type.
Reconstructing accurate 3D urban environments requires comprehensive geospatial data.

Purpose of the Study:

To develop an automatic method for 3D building map reconstruction.
To enhance OpenStreetMap data with LiDAR information for improved 3D urban modeling.

Main Methods:

Supplementing OpenStreetMap data with LiDAR data for 3D reconstruction.
Utilizing a convolutional neural network to analyze LiDAR data for missing building attributes.
Training a model on roof images to infer roof types and building heights.

Main Results:

Successfully inferred a mean of 75.57% for building height data and 38.81% for roof data.
The trained model demonstrated the ability to generalize to new urban areas, including those not used in training.
Identified buildings present in LiDAR data but absent in OpenStreetMap.

Conclusions:

The proposed method effectively generates detailed and accurate 3D building maps by integrating OSM and LiDAR data.
Convolutional neural networks are capable of inferring crucial building attributes, significantly improving 3D urban models.
The approach offers a robust solution for automatic 3D urban environment reconstruction.