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Automatic 3D Building Reconstruction from OpenStreetMap and LiDAR Using Convolutional Neural Networks
This paper presents the implementation of an automatic method for the reconstruction of 3D building maps. The core innovation of the proposed method is the supplementation of OpenStreetMap data with LiDAR data to reconstruct 3D urban environments automatically. The only input of the method is the ar...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10007540/ https://www.ncbi.nlm.nih.gov/pubmed/36904648 http://dx.doi.org/10.3390/s23052444 |
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author | Barranquero, Marcos Olmedo, Alvaro Gómez, Josefa Tayebi, Abdelhamid Hellín, Carlos Javier Saez de Adana, Francisco |
author_facet | Barranquero, Marcos Olmedo, Alvaro Gómez, Josefa Tayebi, Abdelhamid Hellín, Carlos Javier Saez de Adana, Francisco |
author_sort | Barranquero, Marcos |
collection | PubMed |
description | This paper presents the implementation of an automatic method for the reconstruction of 3D building maps. The core innovation of the proposed method is the supplementation of OpenStreetMap data with LiDAR data to reconstruct 3D urban environments automatically. The only input of the method is the area that needs to be reconstructed, defined by the enclosing points in terms of the latitude and longitude. First, area data are requested in OpenStreetMap format. However, there are certain buildings and geometries that are not fully received in OpenStreetMap files, such as information on roof types or the heights of buildings. To complete the information that is missing in the OpenStreetMap data, LiDAR data are read directly and analyzed using a convolutional neural network. The proposed approach shows that a model can be obtained with only a few samples of roof images from an urban area in Spain, and is capable of inferring roofs in other urban areas of Spain as well as other countries that were not used to train the model. The results allow us to identify a mean of 75.57% for height data and a mean of 38.81% for roof data. The finally inferred data are added to the 3D urban model, resulting in detailed and accurate 3D building maps. This work shows that the neural network is able to detect buildings that are not present in OpenStreetMap for which in LiDAR data are available. In future work, it would be interesting to compare the results of the proposed method with other approaches for generating 3D models from OSM and LiDAR data, such as point cloud segmentation or voxel-based approaches. Another area for future research could be the use of data augmentation techniques to increase the size and robustness of the training dataset. |
format | Online Article Text |
id | pubmed-10007540 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100075402023-03-12 Automatic 3D Building Reconstruction from OpenStreetMap and LiDAR Using Convolutional Neural Networks Barranquero, Marcos Olmedo, Alvaro Gómez, Josefa Tayebi, Abdelhamid Hellín, Carlos Javier Saez de Adana, Francisco Sensors (Basel) Article This paper presents the implementation of an automatic method for the reconstruction of 3D building maps. The core innovation of the proposed method is the supplementation of OpenStreetMap data with LiDAR data to reconstruct 3D urban environments automatically. The only input of the method is the area that needs to be reconstructed, defined by the enclosing points in terms of the latitude and longitude. First, area data are requested in OpenStreetMap format. However, there are certain buildings and geometries that are not fully received in OpenStreetMap files, such as information on roof types or the heights of buildings. To complete the information that is missing in the OpenStreetMap data, LiDAR data are read directly and analyzed using a convolutional neural network. The proposed approach shows that a model can be obtained with only a few samples of roof images from an urban area in Spain, and is capable of inferring roofs in other urban areas of Spain as well as other countries that were not used to train the model. The results allow us to identify a mean of 75.57% for height data and a mean of 38.81% for roof data. The finally inferred data are added to the 3D urban model, resulting in detailed and accurate 3D building maps. This work shows that the neural network is able to detect buildings that are not present in OpenStreetMap for which in LiDAR data are available. In future work, it would be interesting to compare the results of the proposed method with other approaches for generating 3D models from OSM and LiDAR data, such as point cloud segmentation or voxel-based approaches. Another area for future research could be the use of data augmentation techniques to increase the size and robustness of the training dataset. MDPI 2023-02-22 /pmc/articles/PMC10007540/ /pubmed/36904648 http://dx.doi.org/10.3390/s23052444 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Barranquero, Marcos Olmedo, Alvaro Gómez, Josefa Tayebi, Abdelhamid Hellín, Carlos Javier Saez de Adana, Francisco Automatic 3D Building Reconstruction from OpenStreetMap and LiDAR Using Convolutional Neural Networks |
title | Automatic 3D Building Reconstruction from OpenStreetMap and LiDAR Using Convolutional Neural Networks |
title_full | Automatic 3D Building Reconstruction from OpenStreetMap and LiDAR Using Convolutional Neural Networks |
title_fullStr | Automatic 3D Building Reconstruction from OpenStreetMap and LiDAR Using Convolutional Neural Networks |
title_full_unstemmed | Automatic 3D Building Reconstruction from OpenStreetMap and LiDAR Using Convolutional Neural Networks |
title_short | Automatic 3D Building Reconstruction from OpenStreetMap and LiDAR Using Convolutional Neural Networks |
title_sort | automatic 3d building reconstruction from openstreetmap and lidar using convolutional neural networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10007540/ https://www.ncbi.nlm.nih.gov/pubmed/36904648 http://dx.doi.org/10.3390/s23052444 |
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