CENAGIS-ALS BENCHMARK - NEW PROPOSAL FOR DENSE ALS BENCHMARK BASED ON THE REVIEW OF DATASETS AND BENCHMARKS FOR 3D POINT CLOUD SEGMENTATION

Benchmarking is an essential tool for scientific and technological progress. This article reviews the benchmarks for 3D point cloud segmentation and classification. Based on the analysis of the articles and the knowledge gathered, it can be concluded that there has been an increase in the number of...

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Main Authors: P. Zachar, K. Bakuła, W. Ostrowski
Format: Article
Language:English
Published: Copernicus Publications 2023-10-01
Series:The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Online Access:https://isprs-archives.copernicus.org/articles/XLVIII-1-W3-2023/227/2023/isprs-archives-XLVIII-1-W3-2023-227-2023.pdf
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author P. Zachar
K. Bakuła
W. Ostrowski
author_facet P. Zachar
K. Bakuła
W. Ostrowski
author_sort P. Zachar
collection DOAJ
description Benchmarking is an essential tool for scientific and technological progress. This article reviews the benchmarks for 3D point cloud segmentation and classification. Based on the analysis of the articles and the knowledge gathered, it can be concluded that there has been an increase in the number of benchmarks, allowing to compare research results against specific performance metrics independently. However, benchmarks vary regarding the number of classes, spatial size, nomenclature, and class division. In this article, we introduce a new annotated 3D dataset - CENAGIS-ALS Benchmark. We propose a benchmark of highly dense lidar point clouds acquired by Leica CityMapper-2 for the Centre of Warsaw, Poland. The area covers 2 km<sup>2</sup>, and the data has a density of 275 pts/m<sup>2</sup>. The dataset consists of a number of classes that are distinguishable for this type of data. In addition to the basic classes, more specialized classes, important from the perspective of urban space, are also distinguished. Moreover, the division of classes consists of three levels of detail from coarse (e.g., a building) to refined elements (e.g., roofs, chimneys, and other rooftop objects). This benchmark can contribute to geospatial societies, considering the large spatial size of the study area with unified data quality and the higher number of classes with the hierarchical division compared to other benchmarking data.
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spelling doaj.art-2ba3f48c98274d958204e3016a1c26ac2023-10-19T18:30:11ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342023-10-01XLVIII-1-W3-202322723410.5194/isprs-archives-XLVIII-1-W3-2023-227-2023CENAGIS-ALS BENCHMARK - NEW PROPOSAL FOR DENSE ALS BENCHMARK BASED ON THE REVIEW OF DATASETS AND BENCHMARKS FOR 3D POINT CLOUD SEGMENTATIONP. Zachar0K. Bakuła1W. Ostrowski2Warsaw University of Technology, Faculty of Geodesy and Cartography, Department of Photogrammetry, Remote Sensing and Spatial Information Systems, Warsaw, PolandWarsaw University of Technology, Faculty of Geodesy and Cartography, Department of Photogrammetry, Remote Sensing and Spatial Information Systems, Warsaw, PolandWarsaw University of Technology, Faculty of Geodesy and Cartography, Department of Photogrammetry, Remote Sensing and Spatial Information Systems, Warsaw, PolandBenchmarking is an essential tool for scientific and technological progress. This article reviews the benchmarks for 3D point cloud segmentation and classification. Based on the analysis of the articles and the knowledge gathered, it can be concluded that there has been an increase in the number of benchmarks, allowing to compare research results against specific performance metrics independently. However, benchmarks vary regarding the number of classes, spatial size, nomenclature, and class division. In this article, we introduce a new annotated 3D dataset - CENAGIS-ALS Benchmark. We propose a benchmark of highly dense lidar point clouds acquired by Leica CityMapper-2 for the Centre of Warsaw, Poland. The area covers 2 km<sup>2</sup>, and the data has a density of 275 pts/m<sup>2</sup>. The dataset consists of a number of classes that are distinguishable for this type of data. In addition to the basic classes, more specialized classes, important from the perspective of urban space, are also distinguished. Moreover, the division of classes consists of three levels of detail from coarse (e.g., a building) to refined elements (e.g., roofs, chimneys, and other rooftop objects). This benchmark can contribute to geospatial societies, considering the large spatial size of the study area with unified data quality and the higher number of classes with the hierarchical division compared to other benchmarking data.https://isprs-archives.copernicus.org/articles/XLVIII-1-W3-2023/227/2023/isprs-archives-XLVIII-1-W3-2023-227-2023.pdf
spellingShingle P. Zachar
K. Bakuła
W. Ostrowski
CENAGIS-ALS BENCHMARK - NEW PROPOSAL FOR DENSE ALS BENCHMARK BASED ON THE REVIEW OF DATASETS AND BENCHMARKS FOR 3D POINT CLOUD SEGMENTATION
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
title CENAGIS-ALS BENCHMARK - NEW PROPOSAL FOR DENSE ALS BENCHMARK BASED ON THE REVIEW OF DATASETS AND BENCHMARKS FOR 3D POINT CLOUD SEGMENTATION
title_full CENAGIS-ALS BENCHMARK - NEW PROPOSAL FOR DENSE ALS BENCHMARK BASED ON THE REVIEW OF DATASETS AND BENCHMARKS FOR 3D POINT CLOUD SEGMENTATION
title_fullStr CENAGIS-ALS BENCHMARK - NEW PROPOSAL FOR DENSE ALS BENCHMARK BASED ON THE REVIEW OF DATASETS AND BENCHMARKS FOR 3D POINT CLOUD SEGMENTATION
title_full_unstemmed CENAGIS-ALS BENCHMARK - NEW PROPOSAL FOR DENSE ALS BENCHMARK BASED ON THE REVIEW OF DATASETS AND BENCHMARKS FOR 3D POINT CLOUD SEGMENTATION
title_short CENAGIS-ALS BENCHMARK - NEW PROPOSAL FOR DENSE ALS BENCHMARK BASED ON THE REVIEW OF DATASETS AND BENCHMARKS FOR 3D POINT CLOUD SEGMENTATION
title_sort cenagis als benchmark new proposal for dense als benchmark based on the review of datasets and benchmarks for 3d point cloud segmentation
url https://isprs-archives.copernicus.org/articles/XLVIII-1-W3-2023/227/2023/isprs-archives-XLVIII-1-W3-2023-227-2023.pdf
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