scholarly journals Graph-Based Classification and Urban Modeling of Laser Scanning and Imagery: Toward 3D Smart Web Services

2021 ◽  
Vol 14 (1) ◽  
pp. 114
Author(s):  
Slim Namouchi ◽  
Imed Riadh Farah
Keyword(s):  
Geometric Modeling ◽  
Laser Scanning ◽  
Ground Surface ◽  
Urban Environments ◽  
Urban Modeling ◽  
Military Operations ◽  
Remotely Sensed Data ◽  
Reconstruction Process ◽  
Interchange Format ◽  
Smart Service

Recently, remotely sensed data obtained via laser technology has gained great importance due to its wide use in several fields, especially in 3D urban modeling. In fact, 3D city models in urban environments are efficiently employed in many fields, such as military operations, emergency management, building and height mapping, cadastral data upgrading, monitoring of changes as well as virtual reality. These applications are essentially composed of models of structures, urban elements, ground surface and vegetation. This paper presents a workflow for modeling the structure of buildings by using laser-scanned data (LiDAR) and multi-spectral images in order to develop a 3D web service for a smart city concept. Optical vertical photography is generally utilized to extract building class, while LiDAR data is used as a source of information to create the structure of the 3D building. The building reconstruction process presented in this study can be divided into four main stages: building LiDAR points extraction, piecewise horizontal roof clustering, boundaries extraction and 3D geometric modeling. Finally, an architecture for a 3D smart service based on the CityGML interchange format is proposed.

scholarly journals AN EFFICIENT DEEP LEARNING APPROACH FOR GROUND POINT FILTERING IN AERIAL LASER SCANNING POINT CLOUDS

2021 ◽  
Vol XLIII-B1-2021 ◽  
pp. 31-38
Author(s):  
A. Nurunnabi ◽  
F. N. Teferle ◽  
J. Li ◽  
R. C. Lindenbergh ◽  
A. Hunegnaw
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Ground Surface ◽  
Point Clouds ◽  
Urban Environments ◽  
Training Data ◽  
Ground Point ◽  
End To End

Abstract. Ground surface extraction is one of the classic tasks in airborne laser scanning (ALS) point cloud processing that is used for three-dimensional (3D) city modelling, infrastructure health monitoring, and disaster management. Many methods have been developed over the last three decades. Recently, Deep Learning (DL) has become the most dominant technique for 3D point cloud classification. DL methods used for classification can be categorized into end-to-end and non end-to-end approaches. One of the main challenges of using supervised DL approaches is getting a sufficient amount of training data. The main advantage of using a supervised non end-to-end approach is that it requires less training data. This paper introduces a novel local feature-based non end-to-end DL algorithm that generates a binary classifier for ground point filtering. It studies feature relevance, and investigates three models that are different combinations of features. This method is free from the limitations of point clouds’ irregular data structure and varying data density, which is the biggest challenge for using the elegant convolutional neural network. The new algorithm does not require transforming data into regular 3D voxel grids or any rasterization. The performance of the new method has been demonstrated through two ALS datasets covering urban environments. The method successfully labels ground and non-ground points in the presence of steep slopes and height discontinuity in the terrain. Experiments in this paper show that the algorithm achieves around 97% in both F1-score and model accuracy for ground point labelling.

Complex Battlespaces

2018 ◽  
Keyword(s):  
International Law ◽  
New Technologies ◽  
Urban Environments ◽  
Armed Conflicts ◽  
Unmanned Aerial Systems ◽  
International Human Rights Law ◽  
Humanitarian Law ◽  
Military Operations ◽  
Modern Warfare ◽  
Aerial Systems

The conduct of warfare is constantly shaped by forces beyond the battlefield. These forces create complexities in the battlespace for military operations. The ever-changing nature of how and where wars are fought creates challenges for the application of the unchanging body of international law that regulates armed conflicts. The term “complex” is often used to describe modern warfare, but what makes modern warfare complex? Is it the increasingly urbanized battlefield where wars are fought, which is cluttered with civilians and civilian objects? Is it the rise of State-like organized armed groups that leverage the governance vacuum created by failed or failing States? Is it the introduction of new technologies to military operations like autonomous weapons, cyber capabilities, and unmanned aerial systems? Or is it the application of multiple legal regimes to a single conflict? Collectively, these questions formed the basis for the Complex Battlespaces Workshop in which legal scholars and experts from the field of practice came together to discuss these complexities. During the workshop, there was a general consensus that the existing law was sufficient to regulate modern warfare. The challenge, however, arises in application of the law to new technologies, military operations in urban environments, and other issues related to applying international human rights law and international humanitarian law to non-international armed conflicts. This inaugural volume of the Lieber Book Series seeks to address many of the complexities that arise during the application of international law to modern warfare.

scholarly journals Ground thermal and geomechanical conditions in a permafrost-affected high-latitude rockslide site (Polvartinden, Northern Norway)

2016 ◽  
Author(s):  
Regula Frauenfelder ◽  
Ketil Isaksen ◽  
Jeannette Nötzli ◽  
Matthew J. Lato
Keyword(s):  
Temperature Regime ◽  
Laser Scanning ◽  
Ground Surface ◽  
Failure Zone ◽  
Climate Data ◽  
Conduction Model ◽  
Near Surface ◽  
Northern Norway ◽  
Surface Temperatures

Abstract. In June 2008, a rockslide detached in the northeast facing slope of Polvartinden, a high-alpine mountain in Signaldalen, Northern Norway. Here, we report on the observed and modelled past and present near-surface temperature regime close to the failure zone, as well as on a subsequent simulation of the subsurface temperature regime, and on initial geomechanical mapping based on laser scanning. The volume of the rockslide was estimated to be approximately 500 000 m3. The depth to the actual failure surface was found to range from 40 m at the back of the failure zone to 0 m at its toe. Visible in-situ ice was observed in the failure zone just after the rockslide. Between September 2009 and August 2013 ground surface temperatures were measured with miniature temperature data loggers at fourteen different localities close to the original failure zone along the northern ridge of Polvartinden, and in the valley floor. The results from these measurements and from a basic three-dimensional heat conduction model suggest that the lower altitudinal limit of permafrost at present is at 600–650 m a.s.l., which corresponds to the upper limit of the failure zone. A coupling of our in-situ data with regional climate data since 1958 suggests a general gradual warming and that a period with highest mean near surface temperatures on record ended four months before the Signaldalen rockslide detached. A comparison with a transient permafrost model run at 10 m depth, representative for areas where snow accumulates, strengthen this findings, which are also in congruence with measurements in nearby permafrost boreholes. It is likely that permafrost in and near the failure zone is presently subject to degradation. This degradation, in combination with the extreme warm year antecedent to the rock failure, is seen to have played an important role in the detaching of the Signaldalen rockslide.

A comparison of UAV laser scanning, photogrammetry and airborne laser scanning for precision inventory of small-forest properties

2019 ◽  
Vol 93 (1) ◽  
pp. 150-162 ◽  
Author(s):  
Stefano Puliti ◽  
Jonathan P Dash ◽  
Michael S Watt ◽  
Johannes Breidenbach ◽  
Grant D Pearse
Keyword(s):  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Stem Volume ◽  
Stem Density ◽  
Multiple Sources ◽  
Remotely Sensed Data ◽  
Biophysical Parameters ◽  
Auxiliary Data ◽  
Airborne Laser ◽  
Small Forest

Abstract This study addresses the use of multiple sources of auxiliary data from unmanned aerial vehicles (UAVs) and airborne laser scanning (ALS) data for inference on key biophysical parameters in small forest properties (5–300 ha). We compared the precision of the estimates using plot data alone under a design-based inference with model-based estimates that include plot data and the following four types of auxiliary data: (1) terrain-independent variables from UAV photogrammetric data (UAV-SfM); (2) variables obtained from UAV photogrammetric data normalized using external terrain data (UAV-SfMDTM); (3) UAV-LS and (4) ALS data. The inclusion of remotely sensed data increased the precision of DB estimates by factors of 1.5–2.2. The optimal data sources for top height, stem density, basal area and total stem volume were: UAV-LS, UAV-SfM, UAV-SfMDTM and UAV-SfMDTM. We conclude that the use of UAV data can increase the precision of stand-level estimates even under intensive field sampling conditions.

scholarly journals Accuracy of Ground Surface Interpolation from Airborne Laser Scanning (ALS) Data in Dense Forest Cover

2020 ◽  
Vol 9 (4) ◽  
pp. 224
Author(s):  
Mihnea Cățeanu ◽  
Arcadie Ciubotaru
Keyword(s):  
Thin Plate ◽  
Laser Scanning ◽  
Ground Surface ◽  
Forest Vegetation ◽  
Airborne Laser Scanning ◽  
Canopy Density ◽  
Digital Terrain ◽  
Airborne Laser ◽  
Dense Forest

A digital model of the ground surface has many potential applications in forestry. Nowadays, Light Detection and Ranging (LiDAR) is one of the main sources for collecting morphological data. Point clouds obtained via laser scanning are used for modelling the ground surface by interpolation, a process which is affected by various errors. Using LiDAR data to collect ground surface data for forestry applications is a challenging scenario because the presence of forest vegetation will hinder the ability of laser pulses to reach the ground. The density of ground observations will be therefore reduced and not homogenous (as it is affected by the variations in canopy density). Furthermore, forest areas are generally present in mountainous areas, in which case the interpolation of the ground surface is more challenging. In this paper, we present a comparative analysis of interpolation accuracy for nine algorithms, which are used for generating Digital Terrain Models from Airborne Laser Scanning (ALS) data, in mountainous terrain covered by dense forest vegetation. For most of the algorithms we find a similar performance in terms of general accuracy, with RMSE values between 0.11 and 0.28 m (when model resolution is set to 0.5 m). Five of the algorithms (Natural Neighbour, Delauney Triangulation, Multilevel B-Spline, Thin-Plate Spline and Thin-Plate Spline by TIN) have vertical errors of less than 0.20 m for over 90 percent of validation points. Meanwhile, for most algorithms, major vertical errors (of over 1 m) are associated with less than 0.05 percent of validation points. Digital Terrain Model (DTM) resolution, ground slope and point cloud density influence the quality of the ground surface model, while for canopy density we find a less significant link with the quality of the interpolated DTMs.

scholarly journals Deformation Monitoring of Earth Fissure Hazards Using Terrestrial Laser Scanning

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1463 ◽  
Author(s):  
Yunfeng Ge ◽  
Huiming Tang ◽  
Xulong Gong ◽  
Binbin Zhao ◽  
Yi Lu ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Ground Surface ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Vertical Displacement ◽  
Deformation Monitoring ◽  
Earth Fissure ◽  
Displacement Analysis ◽  
Remote Sensing Technique ◽  
Noise Data

Deformation monitoring is a powerful tool to understand the formation mechanism of earth fissure hazards, enabling the engineering and planning efforts to be more effective. To assess the evolution characteristics of the Yangshuli earth fissure hazard more completely, terrestrial laser scanning (TLS), a remote sensing technique which is regarded as one of the most promising surveying technologies in geohazard monitoring, was employed to detect the changes to ground surfaces and buildings in small- and large-scales, respectively. Time-series of high-density point clouds were collected through 5 sequential scans from 2014 to 2017 and then pre-processing was performed to filter the noise data of point clouds. A tiny deformation was observed on both the scarp and the walls, based on the local displacement analysis. The relative height differences between the two sides of the scarp increase slowly from 0.169 m to 0.178 m, while no obvious inclining (the maximum tilt reaches just to 0.0023) happens on the two walls, based on tilt measurement. Meanwhile, global displacement analysis indicates that the overall settlement slowly increases for the ground surface, but the regions in the left side of scarp are characterized by a relatively larger vertical displacement than the right. Furthermore, the comparisons of monitoring results on the same measuring line are discussed in this study and TLS monitoring results have an acceptable consistency with the global positioning system (GPS) measurements. The case study shows that the TLS technique can provide an adequate solution in deformation monitoring of earth fissure hazards, with high effectiveness and applicability.

scholarly journals Ground subsidence and heave over permafrost: hourly time series reveal interannual, seasonal and shorter-term movement caused by freezing, thawing and water movement

2020 ◽  
Vol 14 (4) ◽  
pp. 1437-1447 ◽  
Author(s):  
Stephan Gruber
Keyword(s):  
Water Movement ◽  
Ground Surface ◽  
Ground Subsidence ◽  
Late Winter ◽  
Remotely Sensed Data ◽  
Freezing And Thawing ◽  
Frozen Ground ◽  
Process Understanding ◽  
Winter Peak ◽  
Warm Permafrost

Abstract. Heave and subsidence of the ground surface can offer insight into processes of heat and mass transfer in freezing and thawing soils. Additionally, subsidence is an important metric for monitoring and understanding the transformation of permafrost landscapes under climate change. Corresponding ground observations, however, are sparse and episodic. A simple tilt-arm apparatus with logging inclinometer has been developed to measure heave and subsidence of the ground surface with hourly resolution and millimeter accuracy. This contribution reports data from the first two winters and the first full summer, measured at three sites with contrasting organic and frost-susceptible soils in warm permafrost. The patterns of surface movement differ significantly between sites and from a prediction based on the Stefan equation and observed ground temperature. The data are rich in features of heave and subsidence that are several days to several weeks long and that may help elucidate processes in the ground. For example, late-winter heave followed by thawing and subsidence, as reported in earlier literature and hypothesized to be caused by infiltration and refreezing of water into permeable frozen ground, has been detected. An early-winter peak in heave, followed by brief subsidence, is discernible in a previous publication but so far has not been interpreted. An effect of precipitation on changes in surface elevation can be inferred with confidence. These results highlight the potential of ground-based observation of subsidence and heave as an enabler of progress in process understanding, modeling and interpretation of remotely sensed data.

Automated extraction of ground surface along urban roads from mobile laser scanning point clouds

2015 ◽  
Vol 7 (2) ◽  
pp. 170-179 ◽  
Author(s):  
Bin Wu ◽  
Bailang Yu ◽  
Chang Huang ◽  
Qiusheng Wu ◽  
Jianping Wu
Keyword(s):  
Laser Scanning ◽  
Ground Surface ◽  
Point Clouds ◽  
Automated Extraction

scholarly journals Distribution Statistics Preserving Post-Processing Method With Plot Level Uncertainty Analysis for Remotely Sensed Data-Based Forest Inventory Predictions

2018 ◽  
Vol 10 (11) ◽  
pp. 1677
Author(s):  
Virpi Junttila ◽  
Tuomo Kauranne
Keyword(s):  
Forest Inventory ◽  
Laser Scanning ◽  
Linear Models ◽  
Accuracy Assessment ◽  
Remotely Sensed ◽  
Processing Method ◽  
Post Processing ◽  
Remotely Sensed Data ◽  
Training Set ◽  
Study Sites

Remotely sensed data-based models used in operational forest inventory usually give precise and accurate predictions on average, but they often suffer from systematic under- or over-estimation of extreme attribute values resulting in too narrow or skewed attribute distributions. We use a post-processing method based on the statistics of a proper, representative training set to correct the predictions and their probability intervals, attaining corrected predictions that reproduce the statistics of the whole population. Performance of the method is validated with three forest attributes from seven study sites in Finland with training set sizes from 50 to over 400 field plots. The results are compared to those of the uncorrected predictions given by linear models using airborne laser scanning data. The post-processing method improves the accuracy assessment linear fit between the predictions and the reference set by 35.4–51.8% and the distribution fit by 44.5–95.0%. The prediction root mean square error declines on the average by 6.3%. The systematic under- and over-estimation are reduced consistently with all training set sizes. The level of uncertainty is maintained well as the probability intervals cover the real uncertainty while keeping the average probability interval width similar to the one in uncorrected predictions.

scholarly journals Very High Density Point Clouds from UAV Laser Scanning for Automatic Tree Stem Detection and Direct Diameter Measurement

2020 ◽  
Vol 12 (8) ◽  
pp. 1236 ◽  
Author(s):  
Karel Kuželka ◽  
Martin Slavík ◽  
Peter Surový
Keyword(s):  
Cross Sections ◽  
Hough Transform ◽  
Laser Scanning ◽  
Point Clouds ◽  
Mean Bias Error ◽  
Bias Error ◽  
Least Trimmed Squares ◽  
Remotely Sensed Data ◽  
Individual Tree ◽  
Tree Stem

Three-dimensional light detection and ranging (LiDAR) point clouds acquired from unmanned aerial vehicles (UAVs) represent a relatively new type of remotely sensed data. Point cloud density of thousands of points per square meter with survey-grade accuracy makes the UAV laser scanning (ULS) a very suitable tool for detailed mapping of forest environment. We used RIEGL VUX-SYS to scan forest stands of Norway spruce and Scots pine, the two most important economic species of central European forests, and evaluated the suitability of point clouds for individual tree stem detection and stem diameter estimation in a fully automated workflow. We segmented tree stems based on point densities in voxels in subcanopy space and applied three methods of robust circle fitting to fit cross-sections along the stems: (1) Hough transform; (2) random sample consensus (RANSAC); and (3) robust least trimmed squares (RLTS). We detected correctly 99% and 100% of all trees in research plots for spruce and pine, respectively, and were able to estimate diameters for 99% of spruces and 98% of pines with mean bias error of −0.1 cm (−1%) and RMSE of 6.0 cm (19%), using the best performing method, RTLS. Hough transform was not able to fit perimeters in unfiltered and often incomplete point representations of cross-sections. In general, RLTS performed slightly better than RANSAC, having both higher stem detection success rate and lower error in diameter estimation. Better performance of RLTS was more pronounced in complicated situations, such as incomplete and noisy point structures, while for high-quality point representations, RANSAC provided slightly better results.

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