Semi-automatic extraction of rock mass structural data from high resolution LIDAR point clouds

High resolution 3d point cloud data obtained by 3d laser scanning system has become a research hotspot and difficulty in recent years due to its large data volume, irregular data and high scene complexity. Target detection is the basis of scene analysis and understanding, which provides the underlying object and analysis basis for high-level scene understanding. Based on high resolution three-dimensional point cloud data of target recognition and tracking problem both in theory and application is facing great challenge, is a new research topic in this paper, according to the laser point cloud data processing as the research object, analyses the characteristics of lidar point cloud data and data processing of train of thought, analysis of lidar point cloud data storage and retrieval strategy, on the basis of the target recognition based on the laser point cloud data. The lidar data are distributed discretely in form. The discretization here refers to the irregular distribution of the positions and intervals of exponential data points in the three-dimensional space, namely the irregular distribution of data. In recent years, with the rise of deep learning and the large-scale application of deep learning in image detection, speech recognition, text processing and other related fields, it has become one of the current important research topics to use the method of deep learning for target recognition of three-dimensional point cloud data. Its main idea is to learn hierarchical feature expression through supervised way and describe the object from the bottom to the top. This method can effectively improve the ability of object feature representation and the performance of object recognition. Deep learning is also widely used in object recognition, object detection, scene segmentation and other image processing. Therefore, this paper adopts the method of deep learning to classify and identify 3d objects.

Calibration and validation of aboveground biomass (AGB) (AGB) products retrieved from satellite-borne sensors require accurate AGB estimates across hectare scales (1 to 100ha). Recent studies recommend making use of non-destructive terrestrial laser scanning (TLS) based techniques for individual tree AGB estimation that provide unbiased AGB predictors. However, applying these techniques across large sites and landscapes remains logistically challenging. Unoccupied aerial vehicle laser scanning (UAV-LS) has the potential to address this through the collection of high density point clouds across many hectares, but estimation of individual tree AGB based on these data has been challenging so far, especially in dense tropical canopies. In this study, we investigated how TLS and UAV-LS can be used for this purpose by testing different modelling strategies with data availability and modelling framework requirements. The study included data from four forested sites across three biomes: temperate, wet tropical, and tropical savanna. At each site, coincident TLS and UAV-LS campaigns were conducted. Diameter at breast height (DBH) and tree height were estimated from TLS point clouds. Individual tree AGB was estimated for ≥170 trees per site based on TLS tree point clouds and quantitative structure modelling (QSM), and treated as the best available, non-destructive estimate of AGB in the absence of direct, destructive measurements. Individual trees were automatically segmented from the UAV-LS point clouds using a shortest-path algorithm on the full 3D point cloud. Predictions were evaluated in terms of individual tree root mean square error (RMSE) and population bias, the latter being the absolute difference between total tree sample population TLS QSM estimated AGB and predicted AGB. The application of global allometric scaling models (ASM) at local scale and across data modalities, i.e., field-inventory and light detection and ranging LiDAR metrics, resulted in individual tree prediction errors in the range of reported studies, but relatively high population bias. The use of adjustment factors should be considered to translate between data modalities. When calibrating local models, DBH was confirmed as a strong predictor of AGB, and useful when scaling AGB estimates with field inventories. The combination of UAV-LS derived tree metrics with non-parametric modelling generally produced high individual tree RMSE, but very low population bias of ≤5% across sites starting from 55 training samples. UAV-LS has the potential to scale AGB estimates across hectares with reduced fieldwork time. Overall, this study contributes to the exploitation of TLS and UAV-LS for hectare scale, non-destructive AGB estimation relevant for the calibration and validation of space-borne missions targeting AGB estimation.

Abstract. In the recent years, point cloud technologies, such as Unmanned Aerial Vehicles (UAV), Terrestrial Laser Scanners (TLS), Aerial Laser Scanners (ALS), let alone Mobile Mapping Systems (MMS), have come into the focus of attention and have been a subject of considerable public concern in mapping. Thanks to these new techniques, experts can survey large areas with sufficient and homogenous accuracy with high resolution. It comes from this that there are several areas where the point clouds can be used. One of the possible applications of point clouds is updating land registry maps. Many countries worldwide face the issue that a significant part of their large-scale land registry maps are outdated and inaccurate. One of these countries is Hungary, where more than eighty percent of digital cadastre maps were digitised using analogous maps in a scale range of 1:1000 – 1:4000. In this paper, a novel processing queue is presented to find the footprints of the building. Our solution is based on primarily well-known algorithms (RANSAC, DBSCAN) implemented in open-source Python packages. An automated flow was developed, composed of simple processing steps, to cut the point cloud into wall and roof segments and vectorise the wall points under roofs into building footprints. The algorithms and Python programs were tested in villages where detached houses are typical. Tests were made on three study areas in Hungary and we achieved well-promising results.

Terrestrial laserscanning (TLS), also called ground-based LiDAR (Light Detection And Ranging) is a relatively new method which revolutionised geomorphological research in many domains. However, detailed studies of tidal flats by TLS have not been described in the literature yet. This study aims to fill this methodological gap by the application of TLS at two different locations on the coast of Jiangsu Province, Eastern China, and an assessment of the usability of this method for geomorphological research in such environments. The acquired point clouds are first processed to remove erroneous and noisy points. Subsequently, point clouds are computed to produce polygonal meshes and grid-based digital terrain model (DTM) more commonly used by the scientific community. The accuracy of the measurements is assessed by an analysis of elevation deviations for flat and horizontal concrete blocks. High quality point clouds with point densities of up to 4,000 points/m2 were acquired for a distance of up to 200 m. The data allowed for the detection of small landforms such as tidal channels, creeks and ripples in centimetre and decimetre scale. The point clouds had an average error of approximately 3 mm, however for some few points errors of up to 1.8 cm were detected. Based on the results it can be concluded that TLS can be a useful additional method for geomorphological research on tidal flats due to its ability to describe the landforms from high density point clouds. Repeated scanning could therefore provide data to quantitatively and qualitatively describe geomorphological changes over wider areas and thereby improve the understanding of sedimentation and erosion on tidal flats.

Abstract. Slow moving deep-seated gravitational slope deformations are threatening infrastructure and economic wellbeing in mountainous areas. Accelerating landslides may end up in a catastrophic slope failure in terms of rapid rock avalanches. Continuous landslide monitoring enables the identification of critical acceleration thresholds, which are required in natural hazard management. Among many existing monitoring methods, laser scanning is a cost effective method providing 3D data for deriving three dimensional and areawide displacement vectors at certain morphological structures travelling on top of the landslide. Comparing displacements between selected observation periods allows the spatial interpretation of landslide acceleration or deceleration. This contribution presents five laser scanning datasets of the active Reissenschuh landslide (Tyrol, Austria) acquired by airborne laser scanning (ALS), terrestrial laser scanning (TLS) and Unmanned aerial vehicle Laser Scanning (ULS) sensors. Three observation periods with acquisition dates between 2008 and 2018 are used to derive area-wide displacement vectors. To ensure a most suitable displacement derivation between ALS, TLS and ULS platforms, an analysis investigating point cloud features within varying search radii is carried out, in order to identify a neighbourhood where common surfaces are represented platform independent or differences between the platforms are minimized. Consequent displacement vector estimation is done by ICP-Matching using morphological structures within the high resolution TLS and ULS point cloud. Displacements from the lower resolution ALS point cloud and TLS point cloud were determined using a modified version of the well-known image correlation (IMCORR) method working with point cloud derived shaded relief images combined with digital terrain models (DTM). The interplatform compatible analyses of the multi-temporal laser scanning data allows to quantify the area-wide displacement patterns of the landslide. Furthermore, changes of these displacement patterns over time are assessed area-wide. Spatially varying areas of landslide acceleration and deceleration in the order of ±15 cm a−1 between 2008 and 2017 and an area wide acceleration of up to 20 cm a−1 between 2016 and 2018 are identified. Continuing the existing time series with future ULS acquisitions may enable a more complete and detailed displacement monitoring using entirely represented objects within the point clouds.

Recent advances in 3D laser scanner technology have provided a large amount of accurate geo-information as point clouds. The methods of machine vision and photogrammetry are used in various applications such as medicine, environmental studies, and cultural heritage. Aerial laser scanners (ALS), terrestrial laser scanners (TLS), mobile mapping laser scanners (MLS), and photogrammetric cameras via image matching are the most important tools for producing point clouds. In most applications, the process of point cloud registration is considered to be a fundamental issue. Due to the high volume of initial point cloud data, 3D keypoint detection has been introduced as an important step in the registration of point clouds. In this step, the initial volume of point clouds is converted into a set of candidate points with high information content. Many methods for 3D keypoint detection have been proposed in machine vision, and most of them were based on thresholding the saliency of points, but less attention had been paid to the spatial distribution and number of extracted points. This poses a challenge in the registration process when dealing with point clouds with a homogeneous structure. As keypoints are selected in areas of structural complexity, it leads to an unbalanced distribution of keypoints and a lower registration quality. This research presents an automated approach for 3D keypoint detection to control the quality, spatial distribution, and the number of keypoints. The proposed method generates a quality criterion by combining 3D local shape features, 3D local self-similarity, and the histogram of normal orientation and provides a competency index. In addition, the Octree structure is applied to control the spatial distribution of the detected 3D keypoints. The proposed method was evaluated for the keypoint-based coarse registration of aerial laser scanner and terrestrial laser scanner data, having both cluttered and homogeneous regions. The obtained results demonstrate the proper performance of the proposed method in the registration of these types of data, and in comparison to the standard algorithms, the registration error was diminished by up to 56%.

Glacier mass balance is a key component of glacier monitoring programs. Information on the mass balance of Sawir Mountains is poor due to a dearth of in-situ measurements. This paper introduces the applicability of an ultra-long-range terrestrial laser scanner (TLS) to monitor the mass balance of Muz Taw Glacier, Sawir Mountains, China. The Riegl VZ ® -6000 TLS is exceptionally well-suited for measuring snowy and icy terrain. Here, we use TLS to create repeated high spatiotemporal resolution DEMs, focusing on the annual mass balance (June 2, 2015 to July 25, 2016). According to TLS-derived high spatial resolution point clouds, the front variation (glacier retreat) of Muz Taw Glacier was 9.3 m. The mean geodetic elevation change was 4.55 m at the ablation area. By comparing with glaciological measurements, the glaciological elevation change of individual stakes and the TLS-derived geodetic elevation change of corresponding points matched closely, and the calculated balance was −3.864±0.378 m w.e.. This data indicates that TLS provides accurate results and is therefore suitable to monitor mass balance evolution of Muz Taw Glacier.

Abstract. The quality control, maintenance, and renewal of land registry maps have always been priorities in the surveying profession. Many countries worldwide must face the issue that a significant part of their current digital land registry maps are based on old analogue maps that were digitised without involving any in-situ measurements. A direct consequence of this is that the digitised maps' accuracy leaves much to be desired and lags behind maps based on either correct survey or numerical data. Moreover, the quality of existing digital maps can be characterised by inhomogeneity that highly depends on the location. The final solution to the problem would be to carry out new surveys in the critical areas, but that has been postponed due to the lack of time and excessive costs.However, in recent years, point cloud technologies, such as Unmanned Aerial Vehicles (UAV), Terrestrial Laser Scanners (TLS), Aerial Laser Scanners (ALS), together with Mobile Mapping Systems (MMS), have become the focus of attention in mapping. Thanks to these technologies, experts can survey large areas with the necessary and homogenous accuracy, high resolution, and significantly, very rapidly. It is beyond doubt that these modern technologies benefit the process of updating old and less relevant maps.Another underlying aspect worth considering is the automation in data processing since a massive amount of data needs to be evaluated. Some algorithms and their validation on study areas in Hungary are presented in this paper. Our study focuses on the mapping of buildings using point clouds generated from UAV images.

Abstract. Permafrost landscapes in the Arctic are highly vulnerable to warming, with rapid changes underway. High-resolution remote sensing, especially aerial datasets, offers valuable insights into current permafrost characteristics and thaw dynamics. Here, we present a new dataset of very high resolution orthomosaics, point clouds, and digital surface models that we acquired over permafrost landscapes in northwestern Canada and northern and northwestern Alaska for the purpose of better understanding the impacts of climate change on permafrost landscapes. The imagery was collected with the Modular Aerial Camera System (MACS) during aerial campaigns conducted by the Alfred Wegener Institute in the summers of 2018, 2019, and 2021. The MACS was specifically developed by the German Aerospace Center (DLR) for operation under challenging light conditions in polar environments. It features cameras in the optical and the near-infrared wavelengths with up to a 16 MP resolution. We processed the images to four-band (blue–green–red–near-infrared) orthomosaics and digital surface models with spatial resolutions of 7 to 20 cm as well as 3D point clouds with point densities of up to 41 points m−2. The dataset collection features 102 subprojects from 35 target regions (1.4–161.1 km2 in size). Project sizes range from 4.8 to 336 GB. In total, 3.17 TB were published. The horizontal precision of the datasets is in the range of 1–2 px and vertical precision is better than 0.10 m. The datasets are not radiometrically calibrated. Overall, these very high resolution images and point clouds provide significant opportunities for mapping permafrost landforms and generating detailed training datasets for machine learning, can serve as a baseline for change detection for thermokarst and thermo-erosion processes, and help with upscaling of field measurements to lower-resolution satellite observations. The dataset is available on the PANGAEA repository at https://doi.org/10.1594/PANGAEA.961577 (Rettelbach et al., 2024).

Abstract. Laser scanning systems have been developed to capture very high-resolution 3D point clouds and consequently acquire the object geometry. This object measuring technique has a high capacity for being utilized in a wide variety of applications such as indoor and outdoor modelling. The Terrestrial Laser Scanning (TLS) is used as an important data capturing measurement system to provide high quality point cloud from industrial or built-up environments. However, the static nature of the TLS and complexity of the industrial sites necessitate employing a complementary data capturing system e.g. cameras to fill the gaps in the TLS point cloud caused by occlusions which is very common in complex industrial areas. Moreover, employing images provide better radiometric and edge information. This motivated a joint project to develop a system for automatic and robust co-registration of TLS data and images directly, especially for complex objects. In this paper, the proposed methods for various components of this project including gap detection from point cloud, calculation of initial image capturing configuration, user interface and support system for the image capturing procedures, and co-registration between TLS point cloud and photogrammetric point cloud are presented. The primarily results on a complex industrial environment are promising.

In this paper, three methods (Terrestrial Laser Scanner (TLS), terrestrial Structure from Motion photogrammetry (SfM) and aerial SfM photogrammetry with an Unmanned Aerial Vehicle (UAV)) were evaluated and compared to produce high resolution point clouds and Digital Elevation Models (DEMs) in a semiarid, complex badland area (Los Aguarales) with tourism activities. Geomorphological processes and dynamics were studied at different spatial scales. The preliminary results showed the possibilities of a multiscale approach, using various non-invasive techniques, to assess geomorphological processes. The high resolution of the point clouds, obtained with TLS and terrestrial SfM photogrammetry, allowed preliminary identification of numerous spatial details, although no relevant topographical changes were detected during a short, wet spring period (with rainfall of 200 mm). UAV images allowed work at larger scales (catchment), mapping piping features, and could be seen as a worthwhile tool for time-effective data acquisition from larger areas. The application of different technologies and a multiscale approach to generate high resolution DEMs is a useful technique when carrying out geomorphological studies in semiarid badland areas. However, long term studies will be necessary to verify the suitability of these techniques in such complex landscapes, and quantify topographical changes and erosion rates. Finally, the information obtained with these tools could be used to promote the study area as an interesting geomorphosite with opportunities for tourism.

Heterogeneous rock mass, such as flysch is represented by individual lithological units with different geomechanical parameters. The heterogeneity of the rock mass affects its geotechnical behaviour, which causes difficulties in slope stability, as well as in underground construction. Development of modern ground-based remote sensing techniques, enables measurement and positioning of distant objects. Terrestrial Laser Scanner (TLS) has been in the past years successfully integrated in acquisition of geological features. In case of pulsed TLS, the scanner emits short pulsed beam of light and measures the time-of-flight from reflected object surface in order to compute the distance from objects. The resulted point cloud is georeferenced in the post-processing phase. Terrestrial Laser Scanner also records the intensity of reflectance, which depends on the properties of the scanned surface. Flysch rock mass can be followed in SW Slovenia, therefore a lithology profile in quarry Elerji was chosen to test the applicability of TLS in characterising the heterogeneous rock mass. The selected quarry wall was lithologically logged and scanned with TLS. Some samples along the profile have been collected for X-ray diffraction analysis of minerals. The analysis of point cloud included the examination of differences between intensity values for individual lithological units and determination of parameters, affecting the value. The resulted intensity intervals for sandstones and marlstones have been empirically tested on the same profile with relatively positive results. Findings and further analysis would help geologists determine the general engineering geological properties of flysch rock mass in the field, as well as geomechanical conditions for faster and more accurate decisions in providing support types in underground construction, defining slope stability and long-term solutions for stabilisation of rock wall.

Abstract. Nowadays, Terrestrial Laser Scanning (TLS) technology is gaining popularity in monitoring and predicting the movement of landslide due to the capability of high-speed data capture without requiring a direct contact with the monitored surface. It offers very high density of point cloud data in high resolution and also can be an effective tool in detecting the surface movement of the landslide area. The aim of this research is to determine the optimal level of scanning resolution for landslide monitoring using TLS. The Topcon Geodetic Laser Scanner (GLS) 2000 was used in this research to obtain the three dimensional (3D) point cloud data of the landslide area. Four types of resolution were used during scanning operation which were consist of very high, high, medium and low resolutions. After done with the data collection, the point clouds datasets were undergone the process of registration and filtering using ScanMaster software. After that, the registered point clouds datasets were analyzed using CloudCompare software. Based on the results obtained, the accuracy of TLS point cloud data between picking point manually and computed automatically by ScanMaster software shows the maximum Root Mean Square (RMS) value of coordinate differences were 0.013 m in very high resolution, 0.017 m in high resolution, 0.031 m in medium resolution and 0.052 m in low resolution respectively. Meanwhile, the accuracy of TLS point cloud data between picking point manually and total station data using intersection method shows the maximum RMS values of coordinate differences were 0.013 m in very high resolution, 0.018 m in high resolution, 0.033 m in medium resolution and 0.054 m in low resolution respectively. Hence, it can be concluded that the high or very high resolution is needed for landslide monitoring using Topcon GLS-2000 which can provide more accurate data in slope result, while the low and medium resolutions is not suitable for landslide monitoring due to the accuracy of TLS point cloud data that will decreased when the resolution value is increased.

Abstract. OTF (Off The Shelf) quadro copter systems provide a cost effective (below 2000 Euro), flexible and mobile platform for high resolution point cloud mapping. Various studies showed the full potential of these small and flexible platforms. Especially in very tight and complex 3D environments the automatic obstacle avoidance, low copter weight, long flight times and precise maneuvering are important advantages of these small OTS systems in comparison with larger octocopter systems. This study examines the potential of the DJI Phantom 4 pro series and the Phantom 3A series for within-stand and forest tree crown 3D point cloud mapping using both within stand oblique imaging in different altitude levels and data captured from a nadir perspective. On a test site in Brandenburg/Germany a beach crown was selected and measured with 3 different altitude levels in Point Of Interest (POI) mode with oblique data capturing and deriving one nadir mosaic created with 85/85 % overlap using Drone Deploy automatic mapping software. Three different flight campaigns were performed, one in September 2016 (leaf-on), one in March 2017 (leaf-off) and one in May 2017 (leaf-on) to derive point clouds from different crown structure and phenological situations – covering the leaf-on and leafoff status of the tree crown. After height correction, the point clouds where used with GPS geo referencing to calculate voxel based densities on 50 × 10 × 10 cm voxel definitions using a topological network of chessboard image objects in 0,5 m height steps in an object based image processing environment. Comparison between leaf-off and leaf-on status was done on volume pixel definitions comparing the attributed point densities per volume and plotting the resulting values as a function of distance to the crown center. In the leaf-off status SFM (structure from motion) algorithms clearly identified the central stem and also secondary branch systems. While the penetration into the crown structure is limited in the leaf-on status (the point cloud is a mainly a description of the interpolated crown surface) – the visibility of the internal crown structure in leaf-off status allows to map also the internal tree structure up to and stopping at the secondary branch level system. When combined the leaf-on and leaf-off point clouds generate a comprehensive tree crown structure description that allows a low cost and detailed 3D crown structure mapping and potentially precise biomass mapping and/or internal structural differentiation of deciduous tree species types. Compared to TLS (Terrestrial Laser Scanning) based measurements the costs are neglectable and in the range of 1500–2500 €. This suggests the approach for low cost but fine scale in-situ applications and/or projects where TLS measurements cannot be derived and for less dense forest stands where POI flights can be performed. This study used the in-copter GPS measurements for geo referencing. Better absolute geo referencing results will be obtained with DGPS reference points. The study however clearly demonstrates the potential of OTS very low cost copter systems and the image attributed GPS measurements of the copter for the automatic calculation of complex 3D point clouds in a multi temporal tree crown mapping context.

Terrestrial laser scanning to quantify above-ground biomass of structurally complex coastal wetland vegetation