High-resolution 3D Point Clouds Research Articles

Remote sensing data fusion as a tool for biomass prediction in extensive grasslands invaded by L. polyphyllus

AbstractRemote sensing data fusion is a powerful tool to gain information of quantitative and qualitative vegetation properties on field level. The aim of this study was to develop prediction models from sensor data fusion for fresh and dry matter yield (FMY/DMY) in extensively managed grasslands with variable degree of invasion by Lupinus polyphyllus. Therefore, a terrestrial 3d laser scanner (TLS) and a drone‐based hyperspectral camera was used to collect high resolution 3d point clouds and hyperspectral aerial orthomosaics of four extremely heterogenous grasslands. From 3d point clouds multiple features (vegetation height, sum of voxel, point density and surface structure) were extracted and combined with hyperspectral data to develop an optimized biomass model from random forest regression algorithm to predict FMY and DMY (ntrain = 130, ntest = 33). Models from hyperspectral data solitarily had the lowest prediction performance (FMY: R2 = 0.61, nRMSEr = 17.14; DMY: R2 = 0.59, nRMSEr = 19.37). Higher performance was gained by models derived from 3d laser data (FMY: R2 = 0. 76, nRMSEr = 13.3; DMY: R2 = 0. 74, nRMSEr = 15.1). A fusion of both sensor systems increased the FMY prediction performance up to R2 = 0.8; nRMSEr = 12.02 and the DMY prediction performance to R2 = 0.81 and nRMSEr = 12.06. The fusion of complementary sensor systems can increase the power to predict biomass yields of heterogenous and extensively managed grasslands. It is a novel alternative to labour‐intensive, traditional biomass prediction methods and to remote sensing methods using only single sensor data.

Remote Sensing for Assessing Landslides and Associated Hazards

Multi-platform remote sensing using space-, airborne and ground-based sensors has become essential tools for landslide assessment and disaster-risk prevention. Over the last 30 years, the multiplicity of Earth Observation satellites mission ensures uninterrupted optical and radar imagery archives. With the popularization of Unmanned Aerial Vehicles, free optical and radar imagery with high revisiting time, ground and aerial possibilities to perform high-resolution 3D point clouds and derived digital elevation models, it can make it difficult to choose the appropriate method for risk assessment. The aim of this paper is to review the mainstream remote-sensing methods commonly employed for landslide assessment, as well as processing. The purpose is to understand how remote-sensing techniques can be useful for landslide hazard detection and monitoring taking into consideration several constraints such as field location or costs of surveys. First we focus on the suitability of terrestrial, aerial and spaceborne systems that have been widely used for landslide assessment to underline their benefits and drawbacks for data acquisition, processing and interpretation. Several examples of application are presented such as Interferometry Synthetic Aperture Radar (InSAR), lasergrammetry, Terrestrial Optical Photogrammetry. Some of these techniques are unsuitable for slow moving landslides, others limited to large areas and others to local investigations. It can be complicated to select the most appropriate system. Today, the key for understanding landslides is the complementarity of methods and the automation of the data processing. All the mentioned approaches can be coupled (from field monitoring to satellite images analysis) to improve risk management, and the real challenge is to improve automatic solution for landslide recognition and monitoring for the implementation of near real-time emergency systems.

BUILDING FEATURES RECOGNITION FROM UNCERTAIN 3D LIDAR POINT CLOUDS: A SEMANTIC APPROACH

Abstract. LiDAR technology allows rapid observation of high-resolution and precise 3D point clouds for diverse applications in urban and natural areas. However, uneven density and incomplete point clouds make LiDAR data processing more challenging for the extraction of semantic information on objects and their components. In this paper, we propose a knowledge based semantic reasoning solution for the recognition of building components (e.g. roofs) from segmentation results in the presence of uncertainties in LiDAR point clouds. The proposed solution uses a semantic reasoning approach as well as a similarity evaluation process for object recognition. We apply the proposed method to recognize buildings’ roof styles from a point cloud with uncertainty as a case study.

Optimizing Wireless Sensor Network Installations by Visibility Analysis on 3D Point Clouds

In this paper, a MATLAB tool for the automatic detection of the best locations to install a wireless sensor network (WSN) is presented. The implemented code works directly on high-resolution 3D point clouds and aims to help in positioning sensors that are part of a network requiring inter-visibility, namely, a clear line of sight (LOS). Indeed, with the development of LiDAR and Structure from Motion technologies, there is an opportunity to directly use 3D point cloud data to perform visibility analyses. By doing so, many disadvantages of traditional modelling and analysis methods can be bypassed. The algorithm points out the optimal deployment of devices following mainly two criteria: inter-visibility (using a modified version of the Hidden Point Removal operator) and inter-distance. Furthermore, an option to prioritize significant areas is provided. The proposed method was first validated on an artificial 3D model, and then on a landslide 3D point cloud acquired from terrestrial laser scanning for the real positioning of an ultrawide-band WSN already installed in 2016. The comparison between collected data and data acquired by the WSN installed following traditional patterns has demonstrated its ability for the optimal deployment of a WSN requiring inter-visibility.

Curb Detection and Tracking in Low-Resolution 3D Point Clouds Based on Optimization Framework

Curb detection and tracking is an essential component of autonomous vehicle operation in urban environments. Detecting curbs is a particularly challenging task in urban environments that contain countless dynamic objects. Previous studies have approached curb detection using different types of sensor such as cameras, radar, and LIDAR. Among these, LIDAR sensors have superior advantages in regard to detecting curbs because of their robustness in different weather conditions and they can provide accurate distance measurements. Previous methods based on LIDAR have exploited high-resolution 3D point clouds using high-cost LIDARs. However, the processing of large volumes of information is inefficient for autonomous driving technologies because of real time constraints. This paper presents a novel real-time curb detection and tracking algorithm that makes use of a low-resolution LIDAR. The proposed method consists of three steps. First is the extraction of curb candidates based on Principal Component Analysis (PCA) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Second is the selection of the optimal candidate using an optimization framework. Lastly is the tracking of the detected curbs. In the tracking module, we use a combination of spatial consistency and validation gate to track the curb in the occlusion region with dynamic objects. Experiments on a public dataset show that the proposed method achieved 91.54% and 89.76% $F_{1}$ score on the straight and curved road while running at about 18 ms per frame, thereby outperforming the state-of-the-art by a large margin. In addition, we integrated the proposed method with the localization module of our autonomous driving platform. The localization module integrated with the proposed method reduces the positional and lateral root mean square (RMS) error of the vehicle localization by 3.58% and 6.68% respectively. In addition, we compare this method with a deep neural network based method from the perspective of a safety-critical system such as self-driving cars.

The Contribution of Terrestrial Laser Scanning to the Analysis of Cliff Slope Stability in Sugano (Central Italy)

In this work, we describe a comprehensive approach aimed at assessing the slope stability conditions of a tuff cliff located below the village of Sugano (Central Italy) starting from remote geomechanical analysis on high-resolution 3D point clouds collected by terrestrial laser scanner (TLS) surveys. Firstly, the identification of the main joint systems has been made through both manual and automatic analyses on the 3D slope model resulting from the surveys. Afterwards, the identified joint sets were considered to evaluate the slope stability conditions by attributing safety factor (SF) values to the typical rock blocks whose kinematic was proved as compatible with tests for toppling under two independent triggering conditions: hydrostatic water pressure within the joints and seismic action. The results from the remote investigation of the cliff slope provide geometrical information of the blocks more susceptible to instability and pointed out that limit equilibrium condition can be achieved for potential triggering scenarios in the whole outcropping slope.

Improving Resolution of 3D Surface With Convolutional Neural Networks

High-resolution (HR) 3D point cloud is always desired for smart city. Phase measuring profilometry (PMP) has widely used to generate 3D point cloud. However, due to the limitation of hardware, PMP is usually difficult to obtain HR 3D point cloud. This inspires us to exploit low-resolution (LR) pattern images or LR phase image to generate HR 3D point cloud. Specifically, we attempt to solve this problem using deep learning based super-resolution (SR) methods. We formulate a new deep learning based SR method for 3D point cloud. We show that the proposed SR surely improves the resolution of the reconstructed 3D point cloud. In experiments, we compare the proposed SR with other state-of-the-art SR methods, proving that the proposed SR can yield better quality of the reconstructed 3D point cloud and lower computational cost.

Utilizing UAV and 3D Computer Vision for Visual Inspection of a Large Gravity Dam

Dams are a critical infrastructure system for many communities, but they are also one of the most challenging to inspect. Dams are typically very large and complex structures, and the result is that inspections are often time-intensive and require expensive, specialized equipment and training to provide inspectors with comprehensive access to the structure. The scale and nature of dam inspections also introduces additional safety risks to the inspectors. Unmanned aerial vehicles (UAV) have the potential to address many of these challenges, particularly when used as a data acquisition platform for photogrammetric three-dimensional (3D) reconstruction and analysis, though the nature of both UAV and modern photogrammetric methods necessitates careful planning and coordination for integration. This paper presents a case study on one such integration at the Brighton Dam, a large-scale concrete gravity dam in Maryland, USA. A combination of multiple UAV platforms and multi-scale photogrammetry was used to create two comprehensive and high-resolution 3D point clouds of the dam and surrounding environment at intervals. These models were then assessed for their overall quality, as well as their ability to resolve flaws and defects that were artificially applied to the structure between inspection intervals. The results indicate that the integrated process is capable of generating models that accurately render a variety of defect types with sub-millimeter accuracy. Recommendations for mission planning and imaging specifications are provided as well.

New approaches to high-resolution mapping of marine vertical structures

Vertical walls in marine environments can harbour high biodiversity and provide natural protection from bottom-trawling activities. However, traditional mapping techniques are usually restricted to down-looking approaches which cannot adequately replicate their 3D structure. We combined sideways-looking multibeam echosounder (MBES) data from an AUV, forward-looking MBES data from ROVs and ROV-acquired videos to examine walls from Rockall Bank and Whittard Canyon, Northeast Atlantic. High-resolution 3D point clouds were extracted from each sonar dataset and structure from motion photogrammetry (SfM) was applied to recreate 3D representations of video transects along the walls. With these reconstructions, it was possible to interact with extensive sections of video footage and precisely position individuals. Terrain variables were derived on scales comparable to those experienced by megabenthic individuals. These were used to show differences in environmental conditions between observed and background locations as well as explain spatial patterns in ecological characteristics. In addition, since the SfM 3D reconstructions retained colours, they were employed to separate and quantify live coral colonies versus dead framework. The combination of these new technologies allows us, for the first time, to map the physical 3D structure of previously inaccessible habitats and demonstrates the complexity and importance of vertical structures.

MODELLING LANDSCAPE MORPHODYNAMICS BY TERRESTRIAL PHOTOGRAMMETRY: AN APPLICATION TO BEACH AND FLUVIAL SYSTEMS

Beach and fluvial systems are highly dynamic environments, being constantly modified by the action of different natural and anthropic phenomena. To understand their behaviour and to support a sustainable management of these fragile environments, it is very important to have access to cost-effective tools. These methods should be supported on cutting-edge technologies that allow monitoring the dynamics of the natural systems with high periodicity and repeatability at different temporal and spatial scales instead the tedious and expensive field-work that has been carried out up to date. The work herein presented analyses the potential of terrestrial photogrammetry to describe beach morphology. Data processing and generation of high resolution 3D point clouds and derived DEMs is supported by the commercial Agisoft PhotoScan. Model validation is done by comparison of the differences in the elevation among the photogrammetric point cloud and the GPS data along different beach profiles. Results obtained denote the potential that the photogrammetry 3D modelling has to monitor morphological changes and natural events getting differences between 6 and 25 cm. Furthermore, the usefulness of these techniques to control the layout of a fluvial system is tested by the performance of some modeling essays in a hydraulic pilot channel.

MODELLING LANDSCAPE MORPHODYNAMICS BY TERRESTRIAL PHOTOGRAMMETRY: AN APPLICATION TO BEACH AND FLUVIAL SYSTEMS

Abstract. Beach and fluvial systems are highly dynamic environments, being constantly modified by the action of different natural and anthropic phenomena. To understand their behaviour and to support a sustainable management of these fragile environments, it is very important to have access to cost-effective tools. These methods should be supported on cutting-edge technologies that allow monitoring the dynamics of the natural systems with high periodicity and repeatability at different temporal and spatial scales instead the tedious and expensive field-work that has been carried out up to date. The work herein presented analyses the potential of terrestrial photogrammetry to describe beach morphology. Data processing and generation of high resolution 3D point clouds and derived DEMs is supported by the commercial Agisoft PhotoScan. Model validation is done by comparison of the differences in the elevation among the photogrammetric point cloud and the GPS data along different beach profiles. Results obtained denote the potential that the photogrammetry 3D modelling has to monitor morphological changes and natural events getting differences between 6 and 25 cm. Furthermore, the usefulness of these techniques to control the layout of a fluvial system is tested by the performance of some modeling essays in a hydraulic pilot channel.

MORPHOLOGICAL CHANGES ALONG A DIKE LANDSIDE SLOPE SAMPLED BY 4D HIGH RESOLUTION TERRESTRIAL LASER SCANNING

Abstract. Emergence of light detection and ranging (LiDAR) technology provides new tools for geomorphologic studies improving spatial and temporal resolution of data sampling hydrogeological instability phenomena. Specifically, terrestrial laser scanning (TLS) collects high resolution 3D point clouds allowing more accurate monitoring of erosion rates and processes, and thus, quantify the geomorphologic change on vertical landforms like dike landside slopes. Even so, TLS captures observations rapidly and automatically but unselectively. In this research, we demonstrate the potential of TLS for morphological change detection, profile creation and time series analysis in an emergency simulation for characterizing and monitoring slope movements in a dike. The experiment was performed near Schellebelle (Belgium) in November 2015, using a Leica Scan Station C10. Wave overtopping and overflow over a dike were simulated whereby the loading conditions were incrementally increased and 14 successful scans were performed. The aim of the present study is to analyse short-term morphological dynamic processes and the spatial distribution of erosion and deposition areas along a dike landside slope. As a result, we are able to quantify the eroded material coming from the impact on the terrain induced by wave overtopping which caused the dike failure in a few minutes in normal storm scenarios (Q = 25 l/s/m) as 1.24 m3. As this shows that the amount of erosion is measurable using close range techniques; the amount and rate of erosion could be monitored to predict dike collapse in emergency situation. The results confirm the feasibility of the proposed methodology, providing scalability to a comprehensive analysis over a large extension of a dike (tens of meters).

MORPHOLOGICAL CHANGES ALONG A DIKE LANDSIDE SLOPE SAMPLED BY 4D HIGH RESOLUTION TERRESTRIAL LASER SCANNING

Emergence of light detection and ranging (LiDAR) technology provides new tools for geomorphologic studies improving spatial and temporal resolution of data sampling hydrogeological instability phenomena. Specifically, terrestrial laser scanning (TLS) collects high resolution 3D point clouds allowing more accurate monitoring of erosion rates and processes, and thus, quantify the geomorphologic change on vertical landforms like dike landside slopes. Even so, TLS captures observations rapidly and automatically but unselectively. <br><br> In this research, we demonstrate the potential of TLS for morphological change detection, profile creation and time series analysis in an emergency simulation for characterizing and monitoring slope movements in a dike. The experiment was performed near Schellebelle (Belgium) in November 2015, using a Leica Scan Station C10. Wave overtopping and overflow over a dike were simulated whereby the loading conditions were incrementally increased and 14 successful scans were performed. The aim of the present study is to analyse short-term morphological dynamic processes and the spatial distribution of erosion and deposition areas along a dike landside slope. As a result, we are able to quantify the eroded material coming from the impact on the terrain induced by wave overtopping which caused the dike failure in a few minutes in normal storm scenarios (Q = 25 l/s/m) as 1.24 m<sup>3</sup>. As this shows that the amount of erosion is measurable using close range techniques; the amount and rate of erosion could be monitored to predict dike collapse in emergency situation. <br><br> The results confirm the feasibility of the proposed methodology, providing scalability to a comprehensive analysis over a large extension of a dike (tens of meters).

FAST PROBABILISTIC FUSION OF 3D POINT CLOUDS VIA OCCUPANCY GRIDS FOR SCENE CLASSIFICATION

High resolution consumer cameras on Unmanned Aerial Vehicles (UAVs) allow for cheap acquisition of highly detailed images, e.g., of urban regions. Via image registration by means of Structure from Motion (SfM) and Multi View Stereo (MVS) the automatic generation of huge amounts of 3D points with a relative accuracy in the centimeter range is possible. Applications such as semantic classification have a need for accurate 3D point clouds, but do not benefit from an extremely high resolution/density. In this paper, we, therefore, propose a fast fusion of high resolution 3D point clouds based on occupancy grids. The result is used for semantic classification. In contrast to state-of-the-art classification methods, we accept a certain percentage of outliers, arguing that they can be considered in the classification process when a per point belief is determined in the fusion process. To this end, we employ an octree-based fusion which allows for the derivation of outlier probabilities. The probabilities give a belief for every 3D point, which is essential for the semantic classification to consider measurement noise. For an example point cloud with half a billion 3D points (cf. Figure 1), we show that our method can reduce runtime as well as improve classification accuracy and offers high scalability for large datasets.

FAST PROBABILISTIC FUSION OF 3D POINT CLOUDS VIA OCCUPANCY GRIDS FOR SCENE CLASSIFICATION

Abstract. High resolution consumer cameras on Unmanned Aerial Vehicles (UAVs) allow for cheap acquisition of highly detailed images, e.g., of urban regions. Via image registration by means of Structure from Motion (SfM) and Multi View Stereo (MVS) the automatic generation of huge amounts of 3D points with a relative accuracy in the centimeter range is possible. Applications such as semantic classification have a need for accurate 3D point clouds, but do not benefit from an extremely high resolution/density. In this paper, we, therefore, propose a fast fusion of high resolution 3D point clouds based on occupancy grids. The result is used for semantic classification. In contrast to state-of-the-art classification methods, we accept a certain percentage of outliers, arguing that they can be considered in the classification process when a per point belief is determined in the fusion process. To this end, we employ an octree-based fusion which allows for the derivation of outlier probabilities. The probabilities give a belief for every 3D point, which is essential for the semantic classification to consider measurement noise. For an example point cloud with half a billion 3D points (cf. Figure 1), we show that our method can reduce runtime as well as improve classification accuracy and offers high scalability for large datasets.

True 3D building reconstruction: façade, roof and overhang modelling from oblique and vertical aerial imagery

Aerial imaging systems increasingly gain oblique viewing capabilities. Through these passive systems, photogrammetric 3D point clouds of a scene become available in addition to traditional vertical 2.5D information. In the field of urban reconstruction, this complementary information seeks for robust and automated fusion methods in order to derive 3D building geometry as well as topology in larger scales. It is sequentially shown how to get from façade planes over building footprints to roof reconstruction including overhangs. Façade planes are extracted from a photogrammetric high-resolution 3D point cloud. Local regression methods in 2D space are used to determine the local direction and a criterion for the local linearity of the point cloud. Based on these two parameters, the 3D point cloud is segmented according to which façade it belongs to. From the segmented point cloud, building footprints are extracted as polygons. Similar to cadaster information, those polygons, along with a traditional digital surface model (DSM), serve for one thing as the basis for overhang determination which is performed by fitting polynoms on the outside of façades and using their inflection points as overhang boundary. For another thing, they serve as roof areas which are segmented, topologically described and geometrically modelled. Again local regression methods are used but this time in 3D space in order to segment roof parts. Subsequently, the roof topology is derived using region growing methods. The final building models hold both, geometrical and topological properties.

Terrestrial Laser Scanning survey of the Sugano cliff (Orvieto, Italy) for slope stability analyses

The Terrestrial Laser Scanner (TLS) survey of the Sugano (TR, Italy) cliff has been performed with the aim of assessing by a remote position the main rock mass jointing features (such as dip/dip direction, spacing, opening, persistency etc). Geostructural analysis has been performed on the high resolution 3D point clouds collected in the frame of a two days survey by combining six survey positions. The results from the remote investigation of the cliff allowed to identify the limit equilibrium conditions for all the studied cases and provide geometrical information (volume, surface etc.) of the blocks more susceptible to instability. Specifically, the kinematic of rock mass blocks was proved compatible with toppling phenomena tests under two independent triggering conditions: (a) hydrostatic water pressure within the joints and (b) seismic action. Safety factor (SF) values were attributed to these blocks in every studied conditions.

3次元高精細点群データを活用した多層マップマッチングによる公道自己位置推定

3次元高精細点群データを活用した多層マップマッチングによる公道自己位置推定

Geological layers detection and characterisation using high resolution 3D point clouds: example of a box-fold in the Swiss Jura Mountains

The detection and characterisation of geological layers, as well as the precise quantification of their geometrical properties, is of primary interest in several domains of geology. Nevertheless, geological data gathering is commonly limited by access issues during fieldwork investigations. Here we present complementary and alternative tools aimed at allowing the investigation of areas with limited accessibility, such as vertical rock faces. We used 3D point clouds acquired from Terrestrial Laser Scanning and terrestrial photogrammetry to: 1) identify and model (reconstruct) the geometry of the geological layers and 2) semi-automatically segment the different lithologies according to their intensity signature. Our results show that the presented procedures are fast, reliable and efficient compared to traditional fieldwork. In particular, the geometrical analysis led to a very detailed characterisation of the bedding planes, which couldn't be targeted with sufficient precision based on fieldwork alone. The lithological mapping procedure, which was obtained through a semi-automatic segmentation process of a single intensity channel, is highly capable since nearly all the layers were correctly attributed to their corresponding lithology. The performance of the segmentation script was closely related to our prior fieldwork investigation, yielding validation of our semi-automatic point cloud segmentation.