scholarly journals A MULTI-HEIGHT LOD1 MODEL OF ALL BUILDINGS IN THE NETHERLANDS

2019 ◽  
Vol IV-4/W8 ◽  
pp. 51-57 ◽  
Author(s):  
B. Dukai ◽  
H. Ledoux ◽  
J. E. Stoter
Keyword(s):  
The Netherlands ◽  
Ground Surface ◽  
Point Clouds ◽  
Quality Parameters ◽  
Spatial Analyses ◽  
Building Models ◽  
Realistic View ◽  
Block Models ◽  
3D City Modelling ◽  
City Models

<p><strong>Abstract.</strong> The 3D representation of buildings with roof shapes (also called LoD2) is popular in the 3D city modelling domain since it provides a realistic view of 3D city models. However, for many application block models of buildings are sufficient or even more suitable. These so called LoD1 models can be reconstructed relatively easily from building footprints and point clouds. But LoD1 representations for the same building can be rather different because of differences in height references used to reconstruct the block models and differences in underlying statistical calculation methods. Users are often not aware of these differences, while these differences may have an impact on the outcome of spatial analyses. To standardise possible variances of LoD1 models and let the users choose the best one for their application, we have developed a LoD1 reconstruction service that generates several heights per building (both for the ground surface and the extrusion height). The building models are generated for all ~10 million buildings in The Netherlands based on footprints of buildings and LiDAR point clouds. The 3D dataset is updated every month automatically. In addition, for each building quality parameters are calculated and made available. This article describes the development of the LoD1 building service and we report on the spatial analysis that we performed on the generated height values.</p>

scholarly journals JOINT SIMULTANEOUS RECONSTRUCTION OF REGULARIZED BUILDING SUPERSTRUCTURES FROM LOW-DENSITY LIDAR DATA USING ICP

2016 ◽  
Vol III-3 ◽  
pp. 371-378 ◽  
Author(s):  
Andreas Wichmann ◽  
Martin Kada
Keyword(s):  
Main Roof ◽  
Point Clouds ◽  
Original Position ◽  
Low Density ◽  
Data Set ◽  
Joint Point ◽  
Building Models ◽  
Simultaneous Reconstruction ◽  
Point Density ◽  
City Models

There are many applications for 3D city models, e.g., in visualizations, analysis, and simulations; each one requiring a certain level of detail to be effective. The overall trend goes towards including various kinds of anthropogenic and natural objects therein with ever increasing geometric and semantic details. A few years back, the featured 3D building models had only coarse roof geometry. But nowadays, they are expected to include detailed roof superstructures like dormers and chimneys. Several methods have been proposed for the automatic reconstruction of 3D building models from airborne based point clouds. However, they are usually unable to reliably recognize and reconstruct small roof superstructures as these objects are often represented by only few point measurements, especially in low-density point clouds. In this paper, we propose a recognition and reconstruction approach that overcomes this problem by identifying and simultaneously reconstructing regularized superstructures of similar shape. For this purpose, candidate areas for superstructures are detected by taking into account virtual sub-surface points that are assumed to lie on the main roof faces below the measured points. The areas with similar superstructures are detected, extracted, grouped together, and registered to one another with the Iterative Closest Point (ICP) algorithm. As an outcome, the joint point density of each detected group is increased, which helps to recognize the shape of the superstructure more reliably and in more detail. Finally, all instances of each group of superstructures are modeled at once and transformed back to their original position. Because superstructures are reconstructed in groups, symmetries, alignments, and regularities can be enforced in a straight-forward way. The validity of the approach is presented on a number of example buildings from the Vaihingen test data set.

scholarly journals JOINT SIMULTANEOUS RECONSTRUCTION OF REGULARIZED BUILDING SUPERSTRUCTURES FROM LOW-DENSITY LIDAR DATA USING ICP

2016 ◽  
Vol III-3 ◽  
pp. 371-378
Author(s):  
Andreas Wichmann ◽  
Martin Kada
Keyword(s):  
Main Roof ◽  
Point Clouds ◽  
Original Position ◽  
Low Density ◽  
Data Set ◽  
Joint Point ◽  
Building Models ◽  
Simultaneous Reconstruction ◽  
Point Density ◽  
City Models

There are many applications for 3D city models, e.g., in visualizations, analysis, and simulations; each one requiring a certain level of detail to be effective. The overall trend goes towards including various kinds of anthropogenic and natural objects therein with ever increasing geometric and semantic details. A few years back, the featured 3D building models had only coarse roof geometry. But nowadays, they are expected to include detailed roof superstructures like dormers and chimneys. Several methods have been proposed for the automatic reconstruction of 3D building models from airborne based point clouds. However, they are usually unable to reliably recognize and reconstruct small roof superstructures as these objects are often represented by only few point measurements, especially in low-density point clouds. In this paper, we propose a recognition and reconstruction approach that overcomes this problem by identifying and simultaneously reconstructing regularized superstructures of similar shape. For this purpose, candidate areas for superstructures are detected by taking into account virtual sub-surface points that are assumed to lie on the main roof faces below the measured points. The areas with similar superstructures are detected, extracted, grouped together, and registered to one another with the Iterative Closest Point (ICP) algorithm. As an outcome, the joint point density of each detected group is increased, which helps to recognize the shape of the superstructure more reliably and in more detail. Finally, all instances of each group of superstructures are modeled at once and transformed back to their original position. Because superstructures are reconstructed in groups, symmetries, alignments, and regularities can be enforced in a straight-forward way. The validity of the approach is presented on a number of example buildings from the Vaihingen test data set.

scholarly journals CLASSIFICATION OF AERIAL POINT CLOUDS WITH DEEP LEARNING

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 103-110 ◽  
Author(s):  
E. Özdemir ◽  
F. Remondino
Keyword(s):  
Deep Learning ◽  
Point Clouds ◽  
Primary Data ◽  
Ancillary Data ◽  
Ongoing Research ◽  
Energy Evaluation ◽  
Building Models ◽  
3D Cadastre ◽  
City Models

<p><strong>Abstract.</strong> Due to their usefulness in various implementations, such as energy evaluation, visibility analysis, emergency response, 3D cadastre, urban planning, change detection, navigation, etc., 3D city models have gained importance over the last decades. Point clouds are one of the primary data sources for the generation of realistic city models. Beside model-driven approaches, 3D building models can be directly produced from classified aerial point clouds. This paper presents an ongoing research for 3D building reconstruction based on the classification of aerial point clouds without given ancillary data (e.g. footprints, etc.). The work includes a deep learning approach based on specific geometric features extracted from the point cloud. The methodology was tested on the ISPRS 3D Semantic Labeling Contest (Vaihingen and Toronto point clouds) showing promising results, although partly affected by the low density and lack of points on the building facades for the available clouds.</p>

scholarly journals 3D Building Reconstruction Using Dense Photogrammetric Point Cloud

2016 ◽  
Vol XLI-B3 ◽  
pp. 71-74 ◽  
Author(s):  
S. Malihi ◽  
M. J. Valadan Zoej ◽  
M. Hahn ◽  
M. Mokhtarzade ◽  
H. Arefi
Keyword(s):  
Spatial Data ◽  
Urban Areas ◽  
City Planning ◽  
Three Dimensional ◽  
Point Clouds ◽  
Building Models ◽  
Geometrical Constraints ◽  
Three Dimensional Models ◽  
3D City Modelling ◽  
Building Reconstruction

Three dimensional models of urban areas play an important role in city planning, disaster management, city navigation and other applications. Reconstruction of 3D building models is still a challenging issue in 3D city modelling. Point clouds generated from multi view images of UAV is a novel source of spatial data, which is used in this research for building reconstruction. The process starts with the segmentation of point clouds of roofs and walls into planar groups. By generating related surfaces and using geometrical constraints plus considering symmetry, a 3d model of building is reconstructed. In a refinement step, dormers are extracted, and their models are reconstructed. The details of the 3d reconstructed model are in LoD3 level, with respect to modelling eaves, fractions of roof and dormers.

scholarly journals UNLOCKING POINT CLOUD POTENTIAL: FUSING MLS POINT CLOUDS WITH SEMANTIC 3D BUILDING MODELS WHILE CONSIDERING UNCERTAINTY

2021 ◽  
Vol VIII-4/W2-2021 ◽  
pp. 45-52
Author(s):  
O. Wysocki ◽  
Y. Xu ◽  
U. Stilla
Keyword(s):  
Confidence Interval ◽  
Bayesian Network ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Dynamic Adjustment ◽  
3D City Models ◽  
Building Models ◽  
City Models

Abstract. Throughout the years, semantic 3D city models have been created to depict 3D spatial phenomenon. Recently, an increasing number of mobile laser scanning (MLS) units yield terrestrial point clouds at an unprecedented level. Both dataset types often depict the same 3D spatial phenomenon differently, thus their fusion should increase the quality of the captured 3D spatial phenomenon. Yet, each dataset has modality-dependent uncertainties that hinder their immediate fusion. Therefore, we present a method for fusing MLS point clouds with semantic 3D building models while considering uncertainty issues. Specifically, we show MLS point clouds coregistration with semantic 3D building models based on expert confidence in evaluated metadata quantified by confidence interval (CI). This step leads to the dynamic adjustment of the CI, which is used to delineate matching bounds for both datasets. Both coregistration and matching steps serve as priors for a Bayesian network (BayNet) that performs application-dependent identity estimation. The BayNet propagates uncertainties and beliefs throughout the process to estimate end probabilities for confirmed, unmodeled, and other city objects. We conducted promising preliminary experiments on urban MLS and CityGML datasets. Our strategy sets up a framework for the fusion of MLS point clouds and semantic 3D building models. This framework aids the challenging parallel usage of such datasets in applications such as façade refinement or change detection. To further support this process, we open-sourced our implementation.

scholarly journals 3D Building Reconstruction Using Dense Photogrammetric Point Cloud

2016 ◽  
Vol XLI-B3 ◽  
pp. 71-74 ◽  
Author(s):  
S. Malihi ◽  
M. J. Valadan Zoej ◽  
M. Hahn ◽  
M. Mokhtarzade ◽  
H. Arefi
Keyword(s):  
Spatial Data ◽  
Urban Areas ◽  
City Planning ◽  
Three Dimensional ◽  
Point Clouds ◽  
Building Models ◽  
Geometrical Constraints ◽  
Three Dimensional Models ◽  
3D City Modelling ◽  
Building Reconstruction

Three dimensional models of urban areas play an important role in city planning, disaster management, city navigation and other applications. Reconstruction of 3D building models is still a challenging issue in 3D city modelling. Point clouds generated from multi view images of UAV is a novel source of spatial data, which is used in this research for building reconstruction. The process starts with the segmentation of point clouds of roofs and walls into planar groups. By generating related surfaces and using geometrical constraints plus considering symmetry, a 3d model of building is reconstructed. In a refinement step, dormers are extracted, and their models are reconstructed. The details of the 3d reconstructed model are in LoD3 level, with respect to modelling eaves, fractions of roof and dormers.

Development of the sugar beet quality in the Netherlands since 1980 and introduction of invert sugar as a new parameter for beet quality assessment

2015 ◽  
pp. 95-103 ◽  
Author(s):  
Dirk P. Vermeulen
Keyword(s):  
The Netherlands ◽  
Sugar Beet ◽  
Quality Assessment ◽  
Sugar Content ◽  
Quality Parameters ◽  
Invert Sugar ◽  
Internal Quality ◽  
Glucose Content ◽  
Thick Juice ◽  
Trial Phase

The technological beet quality has been always important for the processors of sugar beet. An investigation into the development of the beet quality in the Netherlands since 1980 has shown that beet quality has improved significantly. Internal quality parameters that are traditionally determined in the beet laboratory, i.e. sugar content, Na, K and -aminoN, all show an improving trend over the years. In the factories, better beet quality has led to lower lime consumption in the juice purification and significantly higher thick juice purity. In 2013, Suiker Unie introduced the serial analysis of the glucose content in beet brei as part of the routine quality assessment of the beet. The invert sugar content is subsequently calculated from glucose content with a new correlation. The background, the trial phase and the first experiences with the glucose analyzer are discussed.

scholarly journals Seismic Damage Semantics on Post-Earthquake LOD3 Building Models Generated by UAS

2021 ◽  
Vol 10 (5) ◽  
pp. 345
Author(s):  
Konstantinos Chaidas ◽  
George Tataris ◽  
Nikolaos Soulakellis
Keyword(s):  
Point Clouds ◽  
Recovery Phase ◽  
Seismic Damage ◽  
Unmanned Aircraft ◽  
Earthquake Scenario ◽  
Geography Markup Language ◽  
3D Point Clouds ◽  
Building Models ◽  
Building Walls ◽  
Oblique Images

In a post-earthquake scenario, the semantic enrichment of 3D building models with seismic damage is crucial from the perspective of disaster management. This paper aims to present the methodology and the results for the Level of Detail 3 (LOD3) building modelling (after an earthquake) with the enrichment of the semantics of the seismic damage based on the European Macroseismic Scale (EMS-98). The study area is the Vrisa traditional settlement on the island of Lesvos, Greece, which was affected by a devastating earthquake of Mw = 6.3 on 12 June 2017. The applied methodology consists of the following steps: (a) unmanned aircraft systems (UAS) nadir and oblique images are acquired and photogrammetrically processed for 3D point cloud generation, (b) 3D building models are created based on 3D point clouds and (c) 3D building models are transformed into a LOD3 City Geography Markup Language (CityGML) standard with enriched semantics of the related seismic damage of every part of the building (walls, roof, etc.). The results show that in following this methodology, CityGML LOD3 models can be generated and enriched with buildings’ seismic damage. These models can assist in the decision-making process during the recovery phase of a settlement as well as be the basis for its monitoring over time. Finally, these models can contribute to the estimation of the reconstruction cost of the buildings.

scholarly journals Imaging ground surface deformations in post-disaster settings via small UAVs

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Richard L. Ybañez ◽  
Audrei Anne B. Ybañez ◽  
Alfredo Mahar Francisco A. Lagmay ◽  
Mario A. Aurelio
Keyword(s):  
Surface Deformation ◽  
Ground Surface ◽  
Point Clouds ◽  
Lateral Spreading ◽  
Fault Movement ◽  
Field Surveys ◽  
3D Point Clouds ◽  
Vertical Displacements ◽  
Resolution Imaging ◽  
Post Disaster

AbstractSmall unmanned aerial vehicles have been seeing increased deployment in field surveys in recent years. Their portability, maneuverability, and high-resolution imaging are useful in mapping surface features that satellite- and plane-mounted imaging systems could not access. In this study, we develop and apply a workplan for implementing UAV surveys in post-disaster settings to optimize the flights for the needs of the scientific team and first responders. Three disasters caused by geophysical hazards and their associated surface deformation impacts were studied implementing this workplan and was optimized based on the target features and environmental conditions. An earthquake that caused lateral spreading and damaged houses and roads near riverine areas were observed in drone images to have lengths of up to 40 m and vertical displacements of 60 cm. Drone surveys captured 2D aerial raster images and 3D point clouds leading to the preservation of these features in soft-sedimentary ground which were found to be tilled over after only 3 months. The point cloud provided a stored 3D environment where further analysis of the mechanisms leading to these fissures is possible. In another earthquake-devastated locale, areas hypothesized to contain the suspected source fault zone necessitated low-altitude UAV imaging below the treeline capturing Riedel shears with centimetric accuracy that supported the existence of extensional surface deformation due to fault movement. In the aftermath of a phreatomagmatic eruption and the formation of sub-metric fissures in nearby towns, high-altitude flights allowed for the identification of the location and dominant NE–SW trend of these fissures suggesting horst-and-graben structures. The workplan implemented and refined during these deployments will prove useful in surveying other post-disaster settings around the world, optimizing data collection while minimizing risk to the drone and the drone operators.

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