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1
Jaalama, Kaisa, Heikki Kauhanen, Aino Keitaanniemi, Toni Rantanen, Juho-Pekka Virtanen, Arttu Julin, Matti Vaaja, Matias Ingman, Marika Ahlavuo, and Hannu Hyyppä. "3D Point Cloud Data in Conveying Information for Local Green Factor Assessment." ISPRS International Journal of Geo-Information 10, no. 11 (November 11, 2021): 762. http://dx.doi.org/10.3390/ijgi10110762.
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The importance of ensuring the adequacy of urban ecosystem services and green infrastructure has been widely highlighted in multidisciplinary research. Meanwhile, the consolidation of cities has been a dominant trend in urban development and has led to the development and implementation of the green factor tool in cities such as Berlin, Melbourne, and Helsinki. In this study, elements of the green factor tool were monitored with laser-scanned and photogrammetrically derived point cloud datasets encompassing a yard in Espoo, Finland. The results show that with the support of 3D point clouds, it is possible to support the monitoring of the local green infrastructure, including elements of smaller size in green areas and yards. However, point clouds generated by distinct means have differing abilities in conveying information on green elements, and canopy covers, for example, might hinder these abilities. Additionally, some green factor elements are more promising for 3D measurement-based monitoring than others, such as those with clear geometrical form. The results encourage the involvement of 3D measuring technologies for monitoring local urban green infrastructure (UGI), also of small scale.
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Mayr, A., M. Rutzinger, and C. Geitner. "MULTITEMPORAL ANALYSIS OF OBJECTS IN 3D POINT CLOUDS FOR LANDSLIDE MONITORING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2 (May 30, 2018): 691–97. http://dx.doi.org/10.5194/isprs-archives-xlii-2-691-2018.
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To date multi-temporal 3D point clouds from close-range sensing are used for landslide and erosion monitoring in an operational manner. Morphological changes are typically derived by calculating distances between points from different acquisition epochs. The identification of the underlying processes resulting in surface changes, however, is often challenging, for example due to the complex surface structures and influences from seasonal vegetation dynamics. We present an approach for object-based 3D landslide monitoring based on topographic LiDAR point cloud time series separating specific surface change types automatically. The workflow removes vegetation and relates surface changes derived from a point cloud time series directly to (i) geomorphological object classes (landslide scarp, eroded area, deposit) and (ii) to individual, spatially contiguous objects (such as parts of the landslide scarp and clods of material moving in the landslide). We apply this approach to a time series of nine point cloud epochs from a slope affected by two shallow landslides. A parameter test addresses the influence of the registration error and the associated level of detection on the magnitude of derived object changes. The results of our case study are in accordance with field observations at the test site as well as conceptual landslide models, where retrogressive erosion of the scarp and downslope movement of the sliding mass are major principles of secondary landslide development. We conclude that the presented methods are well suited to extract information on geomorphological process dynamics from the complex point clouds and aggregate it at different levels of abstraction to assist landslide and erosion assessment.
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Kyriou, Aggeliki, Konstantinos Nikolakopoulos, and Ioannis Koukouvelas. "Timely and Low-Cost Remote Sensing Practices for the Assessment of Landslide Activity in the Service of Hazard Management." Remote Sensing 14, no. 19 (September 22, 2022): 4745. http://dx.doi.org/10.3390/rs14194745.
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Landslides are among the most dangerous and catastrophic events in the world. The increasing progress in remote sensing technology made landslide observations timely, systematic and less costly. In this context, we collected multi-dated data obtained by Unmanned Aerial Vehicle (UAV) campaigns and Terrestrial Laser Scanning (TLS) surveys for the accurate and immediate monitoring of a landslide located in a steep and v-shaped valley, in order to provide operational information concerning the stability of the area to the local authorities. The derived data were processed appropriately, and UAV-based as well as TLS point clouds were generated. The monitoring and assessment of the evolution of the landslide were based on the identification of instability phenomena between the multi-dated UAV and TLS point clouds using the direct cloud-to-cloud comparison and the estimation of the deviation between surface sections. The overall evaluation of the results revealed that the landslide remains active for three years but is progressing particularly slowly. Moreover, point clouds arising from a UAV or a TLS sensor can be effectively utilized for landslide monitoring with comparable accuracies. Nevertheless, TLS point clouds proved to be denser and more appropriate in terms of enhancing the accuracy of the monitoring process. The outcomes were validated using measurements, acquired by the Global Navigation Satellite System (GNSS).
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Liu, Dan, Dajun Li, Meizhen Wang, and Zhiming Wang. "3D Change Detection Using Adaptive Thresholds Based on Local Point Cloud Density." ISPRS International Journal of Geo-Information 10, no. 3 (March 2, 2021): 127. http://dx.doi.org/10.3390/ijgi10030127.
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In recent years, because of highly developed LiDAR (Light Detection and Ranging) technologies, there has been increasing demand for 3D change detection in urban monitoring, urban model updating, and disaster assessment. In order to improve the effectiveness of 3D change detection based on point clouds, an approach for 3D change detection using point-based comparison is presented in this paper. To avoid density variation in point clouds, adaptive thresholds are calculated through the k-neighboring average distance and the local point cloud density. A series of experiments for quantitative evaluation is performed. In the experiments, the influencing factors including threshold, registration error, and neighboring number of 3D change detection are discussed and analyzed. The results of the experiments demonstrate that the approach using adaptive thresholds based on local point cloud density are effective and suitable.
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Gonizzi Barsanti, Sara, Marco Raoul Marini, Saverio Giulio Malatesta, and Adriana Rossi. "Evaluation of Denoising and Voxelization Algorithms on 3D Point Clouds." Remote Sensing 16, no. 14 (July 18, 2024): 2632. http://dx.doi.org/10.3390/rs16142632.
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Proper documentation is fundamental to providing structural health monitoring, damage identification and failure assessment for Cultural Heritage (CH). Three-dimensional models from photogrammetric and laser scanning surveys usually provide 3D point clouds that can be converted into meshes. The point clouds usually contain noise data due to different causes: non-cooperative material or surfaces, bad lighting, complex geometry and low accuracy of the instruments utilized. Point cloud denoising has become one of the hot topics of 3D geometric data processing, removing these noise data to recover the ground-truth point cloud and adding smoothing to the ideal surface. These cleaned point clouds can be converted in volumes with different algorithms, suitable for different uses, mainly for structural analysis. This paper aimed to analyse the geometric accuracy of algorithms available for the conversion of 3D point clouds into volumetric models that can be used for structural analyses through the FEA process. The process is evaluated, highlighting problems and difficulties that lie in poor reconstruction results of volumes from denoised point clouds due to the geometric complexity of the objects.
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Zhang, Ju, Qingwu Hu, Hongyu Wu, Junying Su, and Pengcheng Zhao. "Application of Fractal Dimension of Terrestrial Laser Point Cloud in Classification of Independent Trees." Fractal and Fractional 5, no. 1 (February 1, 2021): 14. http://dx.doi.org/10.3390/fractalfract5010014.
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Tree precise classification and identification of forest species is a core issue of forestry resource monitoring and ecological effect assessment. In this paper, an independent tree species classification method based on fractal features of terrestrial laser point cloud is proposed. Firstly, the terrestrial laser point cloud data of an independent tree is preprocessed to obtain terrestrial point clouds of independent tree canopy. Secondly, the multi-scale box-counting dimension calculation algorithm of independent tree canopy dense terrestrial laser point cloud is proposed. Furthermore, a robust box-counting algorithm is proposed to improve the stability and accuracy of fractal dimension expression of independent tree point cloud, which implementing gross error elimination based on Random Sample Consensus. Finally, the fractal dimension of a dense terrestrial laser point cloud of independent trees is used to classify different types of independent tree species. Experiments on nine independent trees of three types show that the fractal dimension can be stabilized under large density variations, proving that the fractal features of terrestrial laser point cloud can stably express tree species characteristics, and can be used for accurate classification and recognition of forest species.
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Sirmacek, Beril, Roderik Lindenbergh, and Jinhu Wang. "QUALITY ASSESSMENT AND COMPARISON OF SMARTPHONE AND LEICA C10 LASER SCANNER BASED POINT CLOUDS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B5 (June 15, 2016): 581–86. http://dx.doi.org/10.5194/isprs-archives-xli-b5-581-2016.
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3D urban models are valuable for urban map generation, environment monitoring, safety planning and educational purposes. For 3D measurement of urban structures, generally airborne laser scanning sensors or multi-view satellite images are used as a data source. However, close-range sensors (such as terrestrial laser scanners) and low cost cameras (which can generate point clouds based on photogrammetry) can provide denser sampling of 3D surface geometry. Unfortunately, terrestrial laser scanning sensors are expensive and trained persons are needed to use them for point cloud acquisition. A potential effective 3D modelling can be generated based on a low cost smartphone sensor. Herein, we show examples of using smartphone camera images to generate 3D models of urban structures. We compare a smartphone based 3D model of an example structure with a terrestrial laser scanning point cloud of the structure. This comparison gives us opportunity to discuss the differences in terms of geometrical correctness, as well as the advantages, disadvantages and limitations in data acquisition and processing. We also discuss how smartphone based point clouds can help to solve further problems with 3D urban model generation in a practical way. We show that terrestrial laser scanning point clouds which do not have color information can be colored using smartphones. The experiments, discussions and scientific findings might be insightful for the future studies in fast, easy and low-cost 3D urban model generation field.
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Sirmacek, Beril, Roderik Lindenbergh, and Jinhu Wang. "QUALITY ASSESSMENT AND COMPARISON OF SMARTPHONE AND LEICA C10 LASER SCANNER BASED POINT CLOUDS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B5 (June 15, 2016): 581–86. http://dx.doi.org/10.5194/isprsarchives-xli-b5-581-2016.
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3D urban models are valuable for urban map generation, environment monitoring, safety planning and educational purposes. For 3D measurement of urban structures, generally airborne laser scanning sensors or multi-view satellite images are used as a data source. However, close-range sensors (such as terrestrial laser scanners) and low cost cameras (which can generate point clouds based on photogrammetry) can provide denser sampling of 3D surface geometry. Unfortunately, terrestrial laser scanning sensors are expensive and trained persons are needed to use them for point cloud acquisition. A potential effective 3D modelling can be generated based on a low cost smartphone sensor. Herein, we show examples of using smartphone camera images to generate 3D models of urban structures. We compare a smartphone based 3D model of an example structure with a terrestrial laser scanning point cloud of the structure. This comparison gives us opportunity to discuss the differences in terms of geometrical correctness, as well as the advantages, disadvantages and limitations in data acquisition and processing. We also discuss how smartphone based point clouds can help to solve further problems with 3D urban model generation in a practical way. We show that terrestrial laser scanning point clouds which do not have color information can be colored using smartphones. The experiments, discussions and scientific findings might be insightful for the future studies in fast, easy and low-cost 3D urban model generation field.
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Dhruwa, L., and P. K. Garg. "POSITIONAL ACCURACY ASSESSMENT OF FEATURES USING LIDAR POINT CLOUD." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-M-3-2023 (September 5, 2023): 77–80. http://dx.doi.org/10.5194/isprs-archives-xlviii-m-3-2023-77-2023.
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Abstract. Nowadays, Light Detection and Ranging (LiDAR) data acquisition technology is gaining popularity due to its accuracy, precision, and rapid data collection. In recent years, many applications have demanded 3-D models and 3-D mapping for fly-through views of cities. LiDAR data is used to map topographic features as well as the height and density of high-rise objects, such as trees and buildings, on the earth's surface. Although there are numerous traditional surveying and space-based technologies existing to determine the elevation or height of any object are time-consuming, inaccurate, and require additional effort. Therefore, the present study focused on developing a large-scale 3D map and accuracy assessment for existing high-rise features in the study area using a Terrestrial Laser Scanner (TLS). Further, LiDAR point cloud data has been used to estimate the position and elevation of the building. It can acquire data anytime, i.e., day and night, and collects more than 1.5 million points per second. The FARO Scene software has been used to process the data, and the processed data is then automatically registered and verified. The point cloud data's overall registration RMSE error is 36 mm. This file with an extension *.LAS format contains the positional coordinates of the features.The approach provided here for positional accuracy of features with improved accuracy will be helpful for identifying and monitoring the shift and deformations in the buildings and other features. It may also be used for site analysis, planning, and building information modeling.
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del Río-Barral, Pablo, Mario Soilán, Silvia María González-Collazo, and Pedro Arias. "Pavement Crack Detection and Clustering via Region-Growing Algorithm from 3D MLS Point Clouds." Remote Sensing 14, no. 22 (November 19, 2022): 5866. http://dx.doi.org/10.3390/rs14225866.
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Road condition monitoring plays a critical role in transportation infrastructure maintenance and traffic safety assurance. This research introduces a methodology to detect cracks on pavement point clouds acquired with Mobile Laser Scanning systems, which offer more versatility and comprehensive information about the road environment than other specific surveying systems (i.e., profilometers, 3D cameras). The methodology comprises the following steps: (1) Road segmentation; (2) the detection of candidate crack points in individual scanning lines of the point cloud, based on point elevation; (3) crack point clustering via a region-growing algorithm; and (4) crack geometrical attributes extraction. Both the profile evaluation and the region-growing clustering algorithms have been developed from scratch to detect cracks directly from 3D point clouds instead of using raster data or Geo-Referenced Feature images, offering a quick and effective pre-rating tool for pavement condition assessment. Crack detection is validated with data from damaged roads in Portugal.
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Xu, Zhuangzhi, Xin Shen, and Lin Cao. "Extraction of Forest Structural Parameters by the Comparison of Structure from Motion (SfM) and Backpack Laser Scanning (BLS) Point Clouds." Remote Sensing 15, no. 8 (April 19, 2023): 2144. http://dx.doi.org/10.3390/rs15082144.
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Forest structural parameters are key indicators for forest growth assessment, and play a critical role in forest resources monitoring and ecosystem management. Terrestrial laser scanning (TLS) can obtain three-dimensional (3D) forest structures with ultra-high precision without destruction, whereas some shortcomings such as non-portability and cost-consuming can limit the quick and broad acquisition of forest structure. Structure from motion (SfM) and backpack laser scanning (BLS) technology have the advantages of low-cost and high-portability while obtaining 3D structure information of forests. In this study, the high-overlapped images and the BLS point cloud, combined with the point cloud registration and individual tree segmentation to extract the forest structural parameters and compared with the TLS for assessing the accuracy and efficiency of low-cost SfM and portable BLS point clouds. Three plots with different forest structural complexity (coniferous, broadleaf and mixed plot) in the northern subtropical forests were selected. Firstly, portable photography camera, BLS and TLS were used to acquire 3D SfM and LiDAR point clouds, and spatial co-registration of different-sourced point cloud datasets were carried out based on the understory markers. Secondly, the point clouds of individual tree trunk and crown were segmented by the comparative shortest-path algorithm (CSP), and then the height and position of individual tree were extracted based on the tree crown point cloud. Thirdly, the trunk diameter at different heights were calculated by point cloud slices using the density-based spatial clustering of applications with noise (DBSCAN) algorithm, and combined with the stem curve of individual tree which was constructed using four Taper equations to estimate the individual tree volume. Finally, the extraction accuracy of forest structural parameters based on SfM and BLS point clouds were verified and comprehensively compared with field-measured and TLS data. The results showed that: (1) the individual tree segmentation based on SfM and BLS point clouds all performed quite well, among which the segmentation accuracy (F) of SfM point cloud was 0.80 and the BLS point cloud was 0.85; and (2) the accuracy of DBH and tree height extraction based on the SfM and BLS point clouds in comparison with the field-measured data were relatively high. The root mean square error (RMSE) of DBH and tree height extraction based on SfM point cloud were 2.15 cm and 4.08 m, and the RMSE of DBH and tree height extraction based on BLS point cloud were 2.06 cm and 1.63 m. This study shows that with the adopted image capture method, terrestrial SfM photogrammetry can be applied quite well in extracting DBH.
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Matwij, Wojciech, Tomasz Lipecki, and Wojciech Franciszek Jaśkowski. "Selection of an Algorithm for Assessing the Verticality of Complex Slender Objects Using Semi-Automatic Point Cloud Analysis." Remote Sensing 16, no. 3 (January 23, 2024): 435. http://dx.doi.org/10.3390/rs16030435.
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Remote technologies, including laser scanning, are frequently employed to acquire data describing the geometric condition of engineering objects. The automation of point cloud processing becomes essential for promptly and reliably monitoring changes in their current shape. The article introduces a methodology for generating point clouds, focusing on detecting the shape of the object’s cross profiles and subsequently determining its inclination through simulations and real data recorded using terrestrial laser scanning technology. The simulations enabled the identification of variations in the characteristics of changes in the course of the axis of a slender structure, depending on the adopted calculation method. Point clouds derived from measurements of complex engineering objects facilitated the validation of the assumptions of the proposed methodology. The suggested solution enables the semi-automatic extraction of data from point clouds and the assessment of the geometric state of engineering object axes based on multi-temporal point clouds.
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Zhou, Hong, Qingda Li, and Qiuju Xie. "Individual Pig Identification Using Back Surface Point Clouds in 3D Vision." Sensors 23, no. 11 (May 28, 2023): 5156. http://dx.doi.org/10.3390/s23115156.
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The individual identification of pigs is the basis for precision livestock farming (PLF), which can provide prerequisites for personalized feeding, disease monitoring, growth condition monitoring and behavior identification. Pig face recognition has the problem that pig face samples are difficult to collect and images are easily affected by the environment and body dirt. Due to this problem, we proposed a method for individual pig identification using three-dimension (3D) point clouds of the pig’s back surface. Firstly, a point cloud segmentation model based on the PointNet++ algorithm is established to segment the pig’s back point clouds from the complex background and use it as the input for individual recognition. Then, an individual pig recognition model based on the improved PointNet++LGG algorithm was constructed by increasing the adaptive global sampling radius, deepening the network structure and increasing the number of features to extract higher-dimensional features for accurate recognition of different individuals with similar body sizes. In total, 10,574 3D point cloud images of ten pigs were collected to construct the dataset. The experimental results showed that the accuracy of the individual pig identification model based on the PointNet++LGG algorithm reached 95.26%, which was 2.18%, 16.76% and 17.19% higher compared with the PointNet model, PointNet++SSG model and MSG model, respectively. Individual pig identification based on 3D point clouds of the back surface is effective. This approach is easy to integrate with functions such as body condition assessment and behavior recognition, and is conducive to the development of precision livestock farming.
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Aboutalebi, Mahyar, Alfonso F. Torres-Rua, Mac McKee, William P. Kustas, Hector Nieto, Maria Mar Alsina, Alex White, et al. "Incorporation of Unmanned Aerial Vehicle (UAV) Point Cloud Products into Remote Sensing Evapotranspiration Models." Remote Sensing 12, no. 1 (December 20, 2019): 50. http://dx.doi.org/10.3390/rs12010050.
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In recent years, the deployment of satellites and unmanned aerial vehicles (UAVs) has led to production of enormous amounts of data and to novel data processing and analysis techniques for monitoring crop conditions. One overlooked data source amid these efforts, however, is incorporation of 3D information derived from multi-spectral imagery and photogrammetry algorithms into crop monitoring algorithms. Few studies and algorithms have taken advantage of 3D UAV information in monitoring and assessment of plant conditions. In this study, different aspects of UAV point cloud information for enhancing remote sensing evapotranspiration (ET) models, particularly the Two-Source Energy Balance Model (TSEB), over a commercial vineyard located in California are presented. Toward this end, an innovative algorithm called Vegetation Structural-Spectral Information eXtraction Algorithm (VSSIXA) has been developed. This algorithm is able to accurately estimate height, volume, surface area, and projected surface area of the plant canopy solely based on point cloud information. In addition to biomass information, it can add multi-spectral UAV information to point clouds and provide spectral-structural canopy properties. The biomass information is used to assess its relationship with in situ Leaf Area Index (LAI), which is a crucial input for ET models. In addition, instead of using nominal field values of plant parameters, spatial information of fractional cover, canopy height, and canopy width are input to the TSEB model. Therefore, the two main objectives for incorporating point cloud information into remote sensing ET models for this study are to (1) evaluate the possible improvement in the estimation of LAI and biomass parameters from point cloud information in order to create robust LAI maps at the model resolution and (2) assess the sensitivity of the TSEB model to using average/nominal values versus spatially-distributed canopy fractional cover, height, and width information derived from point cloud data. The proposed algorithm is tested on imagery from the Utah State University AggieAir sUAS Program as part of the ARS-USDA GRAPEX Project (Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment) collected since 2014 over multiple vineyards located in California. The results indicate a robust relationship between in situ LAI measurements and estimated biomass parameters from the point cloud data, and improvement in the agreement between TSEB model output of ET with tower measurements when employing LAI and spatially-distributed canopy structure parameters derived from the point cloud data.
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Wang, Xianwei, Yidan Wang, Xionghui Liao, Ying Huang, Yuli Wang, Yibo Ling, and Ting On Chan. "Monitoring of Levee Deformation for Urban Flood Risk Management Using Airborne 3D Point Clouds." Water 16, no. 4 (February 12, 2024): 559. http://dx.doi.org/10.3390/w16040559.
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In the low-lying, river-rich Pearl River Delta in South China, an extensive network of flood defense levees, spanning over 4400 km, plays a crucial role in urban flood management. These levees are designed to withstand floods and storm surges, yet their failure can lead to significant human and economic losses, highlighting the need for robust urban flood defense strategies. This necessitates the development of a sophisticated geographic information system for the levee network and rapid, accurate assessment methods for levee conditions to support water management and flood mitigation efforts. This study focuses on the levees along the Hengmen waterway in the Pearl River Delta, utilizing airborne Light Detection and Ranging (LiDAR) technology to gather 3D spatial data of the levees. Employing the Cloth Simulation Filter (CSF) algorithm, non-ground point cloud data were extracted. The study improved upon the region-growing algorithm, using a seed point set approach for the automatic extraction of levee point cloud data. The accuracy and completeness of levee extraction were evaluated using the quality index. This method achieved effective extraction of four levee types, showing significant improvements over traditional algorithms, with extraction quality ranging from 72% to 83%. Key research outcomes include the development of a novel method for detecting localized levee depressions based on the computation of the variance of angles between normal vectors in single-phase levee point cloud data. An adaptive optimal neighborhood approach was utilized to accurately determine the normal vectors, effectively representing the local morphology of the levee point clouds. Applied in three levee depression detection experiments, this method proved effective, demonstrating the capability of single-phase data in identifying regions of levee depression deformation. This advancement in levee monitoring technology marks a significant step forward in enhancing urban flood defense capabilities in regions such as the cities of the Pearl River Delta in China.
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Zeybek, M., and İ. Şanloğlu. "LANDSLIDE MONITORING AND ASSESSMENT FOR HIGHWAY RETAININGWALL: THE CASE STUDY OF TAŞKENT(TURKEY) LANDSLIDE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W4 (March 6, 2018): 603–8. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w4-603-2018.
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<p><strong>Abstract.</strong> Landslide monitoring and assessment of the highways retaining walls are a crucial task. Because there exist a risk and danger with regard to the movement of the wall to the highway by landslide force that may spread further. To evaluate the changing, movements have to be monitored. For this reason, we practised mobile LiDAR surveys on the landslide effected wall on the highway. The usage of the mobile LiDAR systems have significantly increased in recent years, especially for road management. Currently, mobile LiDAR technology is capable of measuring the earth surface with high precision and density as a 3D point clouds. As stated in this study, the point cloud data processing have been analysed further and the wall surface points fitted to a plane object for monitoring of the landslide effects. This study focuses on different plane fitting algorithms which represents the retaining wall, a performance assessment and evaluation of the deformation between two plane models. The analysis indicates that the uncertainty of the measurements between the two epochs on stable areas survey was within &plusmn;2&thinsp;cm. According to the experimental results, the proposed methods performed promising results that can be used for monitoring of retaining walls for fast processing and assessment.</p>
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Yin, Chao, Haoran Li, Zhinan Hu, and Ying Li. "Application of the Terrestrial Laser Scanning in Slope Deformation Monitoring: Taking a Highway Slope as an Example." Applied Sciences 10, no. 8 (April 18, 2020): 2808. http://dx.doi.org/10.3390/app10082808.
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Slope deformation monitoring is the prerequisite for disaster risk assessment and engineering control. Terrestrial laser scanning (TLS) is highly applicable to this field. Coarse registration method of point cloud based on scale-invariant feature transform (SIFT) feature points and fine registration method based on the k-dimensional tree (K-D tree) improved iterative closest point (ICP) algorithm were proposed. The results show that they were superior to other algorithms (such as speeded-up robust features (SURF) feature points, Harris feature points, and Levenberg-Marquardt (LM) improved ICP algorithm) when taking the Stanford Bunny as an example, and had high applicability in coarse and fine registration. In order to integrate the advantages of point measurement and surface measurement, an improved point cloud comparison method was proposed and the optimal model parameters were determined through model tests. A case study was conducted on the left side of the K146 + 150 point at S236 Boshan section, Shandong Province, and research results show that from 14 August 2018 and 9 November 2019, the overall deformation of the slope was small with a maximum value of 0.183 m, and the slope will continue to maintain a stable state without special inducing factors such as earthquake, heavy rainfall and artificial excavation.
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Chidburee, P., J. P. Mills, P. E. Miller, and K. D. Fieber. "TOWARDS A LOW-COST, REAL-TIME PHOTOGRAMMETRIC LANDSLIDE MONITORING SYSTEM UTILISING MOBILE AND CLOUD COMPUTING TECHNOLOGY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B5 (June 16, 2016): 791–97. http://dx.doi.org/10.5194/isprs-archives-xli-b5-791-2016.
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Close-range photogrammetric techniques offer a potentially low-cost approach in terms of implementation and operation for initial assessment and monitoring of landslide processes over small areas. In particular, the Structure-from-Motion (SfM) pipeline is now extensively used to help overcome many constraints of traditional digital photogrammetry, offering increased user-friendliness to nonexperts, as well as lower costs. However, a landslide monitoring approach based on the SfM technique also presents some potential drawbacks due to the difficulty in managing and processing a large volume of data in real-time. This research addresses the aforementioned issues by attempting to combine a mobile device with cloud computing technology to develop a photogrammetric measurement solution as part of a monitoring system for landslide hazard analysis. The research presented here focusses on (i) the development of an Android mobile application; (ii) the implementation of SfM-based open-source software in the Amazon cloud computing web service, and (iii) performance assessment through a simulated environment using data collected at a recognized landslide test site in North Yorkshire, UK. Whilst the landslide monitoring mobile application is under development, this paper describes experiments carried out to ensure effective performance of the system in the future. Investigations presented here describe the initial assessment of a cloud-implemented approach, which is developed around the well-known VisualSFM algorithm. Results are compared to point clouds obtained from alternative SfM 3D reconstruction approaches considering a commercial software solution (Agisoft PhotoScan) and a web-based system (Autodesk 123D Catch). Investigations demonstrate that the cloud-based photogrammetric measurement system is capable of providing results of centimeter-level accuracy, evidencing its potential to provide an effective approach for quantifying and analyzing landslide hazard at a local-scale.
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Chidburee, P., J. P. Mills, P. E. Miller, and K. D. Fieber. "TOWARDS A LOW-COST, REAL-TIME PHOTOGRAMMETRIC LANDSLIDE MONITORING SYSTEM UTILISING MOBILE AND CLOUD COMPUTING TECHNOLOGY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B5 (June 16, 2016): 791–97. http://dx.doi.org/10.5194/isprsarchives-xli-b5-791-2016.
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Close-range photogrammetric techniques offer a potentially low-cost approach in terms of implementation and operation for initial assessment and monitoring of landslide processes over small areas. In particular, the Structure-from-Motion (SfM) pipeline is now extensively used to help overcome many constraints of traditional digital photogrammetry, offering increased user-friendliness to nonexperts, as well as lower costs. However, a landslide monitoring approach based on the SfM technique also presents some potential drawbacks due to the difficulty in managing and processing a large volume of data in real-time. This research addresses the aforementioned issues by attempting to combine a mobile device with cloud computing technology to develop a photogrammetric measurement solution as part of a monitoring system for landslide hazard analysis. The research presented here focusses on (i) the development of an Android mobile application; (ii) the implementation of SfM-based open-source software in the Amazon cloud computing web service, and (iii) performance assessment through a simulated environment using data collected at a recognized landslide test site in North Yorkshire, UK. Whilst the landslide monitoring mobile application is under development, this paper describes experiments carried out to ensure effective performance of the system in the future. Investigations presented here describe the initial assessment of a cloud-implemented approach, which is developed around the well-known VisualSFM algorithm. Results are compared to point clouds obtained from alternative SfM 3D reconstruction approaches considering a commercial software solution (Agisoft PhotoScan) and a web-based system (Autodesk 123D Catch). Investigations demonstrate that the cloud-based photogrammetric measurement system is capable of providing results of centimeter-level accuracy, evidencing its potential to provide an effective approach for quantifying and analyzing landslide hazard at a local-scale.
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Eboigbe, M. A., and D. B. Kidner. "ASSESSMENT OF THE PRECISION OF A SMART-PHONE POLE PHOTOGRAMMETRY FOR A SECOND-ORDER CLIFF SURFACE DEFORMATION STUDIES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-M-2-2020 (November 17, 2020): 15–24. http://dx.doi.org/10.5194/isprs-archives-xliv-m-2-2020-15-2020.
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Abstract. Coastal cliff is almost a vertical elongated structure with a wave-cut notch and a landslip. Cliffs are geological formations with an almost unpredictable and unstoppable detachment between constitutes formations. Due to health, safety, environmental, and military restrictions, there are more regulations and restrictions on the use of drones. There are also the issues of portability and high cost for the purchase of hybrid drones and Terrestrial Laser Scanners (TLS). These negate the regular monitoring of the coastal cliff. This research develops a rapid, low-cost, and precise digital photogrammetry methodology for the continuous monitoring of the cliff by using the pole as the platform and a mobile phone as a sensor. The most practical vertical camera angle, image overlaps, survey distance to the cliff, and realistic time range for surveys are all determined from the basic surveying principles. Precise geometrically related point clouds generated are with or without the Global Navigation Satellite Systems (GNSS). The standard deviation for “alignment and surface deviation” at every point on each point cloud is ± 0.05 m in the Northing and ± 0.12 m on the Easting’s for the self-calibrated digital camera and without the use of GNSS control points. With the GNSS controls, the maximum deviation in the XYZ coordinates is ± 5 cm. Change analysis performed identifies areas of cut, fill, and the segment of threats in all point clouds. The photogrammetric technique developed is very cheap, simple, and reliable with minimum labor. The results obtained indicate the applicability of this methodology for second-order cliff Deformation study.
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Markiewicz, Jakub. "Evaluation of 2D affine — hand-crafted detectors for feature-based TLS point cloud registration." Reports on Geodesy and Geoinformatics 117, no. 1 (May 25, 2024): 69–88. http://dx.doi.org/10.2478/rgg-2024-0008.
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Abstract The development of modern surveying methods, particularly, Terrestrial Laser Scanning (TLS), has found wide application in protecting and monitoring engineering and objects and sites of cultural heritage. For this reason, it is crucial that several factors a˛ecting the correctness of point cloud registration are considered, including the correctness of the distribution of control points (both signalised and natural), the quality of the process, and robustness analysis. The aim of this article is to evaluate the quality and correctness of TLS registration based on point clouds converted to raster form (in spherical mapping) and hand-crafted detectors. The expanded Structure-from-Motion (SfM) was used to detect the tie points for TLS registration and reliability assessment. The results demonstrated that affine detectors are useful in detecting a high number of key points (increased for point detectors by 8–12 times and for blob detectors by about 10–24 times), improving the quality and TLS registration completeness. For the registration accuracy of point cloud on signalised check points, the lower values can be noted for maximum RMSE errors for blob affine detectors than detectors and larger values for corner detectors and affine detectors (not more than 4 mm in the extreme cases, typically 2 mm). The commonly-applied target-based registration method yields similar results (di˛erences do not exceed – in extreme cases – 3.5 mm, typically less than 2 mm), proving that using affine detectors in the TLS registration process is and reasonable and can be recommended.
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Zhang, Hongwei, Yanjie Zhu, Wen Xiong, and C. S. Cai. "Point Cloud Registration Methods for Long-Span Bridge Spatial Deformation Monitoring Using Terrestrial Laser Scanning." Structural Control and Health Monitoring 2023 (February 9, 2023): 1–16. http://dx.doi.org/10.1155/2023/2629418.
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In recent years, efforts have been devoted to utilizing terrestrial laser scanning for bridge spatial performance inspection, but they are still restricted to small or medium-span bridges, like some historical heritages. Due to the large-scale dimensional features of long-span bridges, applications of 3D point cloud techniques still remain challenging, such as the extra-long scan range and extreme-small incidence angle when scanning a bridge with a span over 1000 m. Moreover, rare attempts can be found for the performance evaluation of point cloud registration methods for long-span bridges as well, which is a critical basis for further spatial deformation recognition on the point cloud data. Hence, in this study, a cross-evaluation of three iterative closest point (ICP) registration methods is conducted for long-span suspension bridges, namely, traditional ICP, kd-tree-based ICP, and feature point-based ICP algorithms. We conducted field laser scanning on the Ma’anshan Yangtze Bridge, an 1880 m long suspension bridge located in China. The results show that the feature point-based ICP algorithm outperforms the other two in terms of convergence rate and execution time for a single iteration due to the smaller number of registration points compared to the other two algorithms. Moreover, it also gives more precise values in terms of bridge tower spatial deformation identification. Meanwhile, due to efficient data organization, the kd-tree-based ICP algorithm takes less time for a single iteration than the traditional ICP algorithm. Finally, two suggestions for algorithm improvement in terms of efficiency optimization and accuracy improvement of long-span bridge deformation analysis are proposed based on the assessment results.
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Kaiser, Soraya, Julia Boike, Guido Grosse, and Moritz Langer. "The Potential of UAV Imagery for the Detection of Rapid Permafrost Degradation: Assessing the Impacts on Critical Arctic Infrastructure." Remote Sensing 14, no. 23 (December 2, 2022): 6107. http://dx.doi.org/10.3390/rs14236107.
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Ground subsidence and erosion processes caused by permafrost thaw pose a high risk to infrastructure in the Arctic. Climate warming is increasingly accelerating the thawing of permafrost, emphasizing the need for thorough monitoring to detect damages and hazards at an early stage. The use of unoccupied aerial vehicles (UAVs) allows a fast and uncomplicated analysis of sub-meter changes across larger areas compared to manual surveys in the field. In our study, we investigated the potential of photogrammetry products derived from imagery acquired with off-the-shelf UAVs in order to provide a low-cost assessment of the risks of permafrost degradation along critical infrastructure. We tested a minimal drone setup without ground control points to derive high-resolution 3D point clouds via structure from motion (SfM) at a site affected by thermal erosion along the Dalton Highway on the North Slope of Alaska. For the sub-meter change analysis, we used a multiscale point cloud comparison which we improved by applying (i) denoising filters and (ii) alignment procedures to correct for horizontal and vertical offsets. Our results show a successful reduction in outliers and a thorough correction of the horizontal and vertical point cloud offset by a factor of 6 and 10, respectively. In a defined point cloud subset of an erosion feature, we derive a median land surface displacement of −0.35 m from 2018 to 2019. Projecting the development of the erosion feature, we observe an expansion to NNE, following the ice-wedge polygon network. With a land surface displacement of −0.35 m and an alignment root mean square error of 0.99 m, we find our workflow is best suitable for detecting and quantifying rapid land surface changes. For a future improvement of the workflow, we recommend using alternate flight patterns and an enhancement of the point cloud comparison algorithm.
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Farmakis, I., D. Bonneau, D. J. Hutchinson, and N. Vlachopoulos. "SUPERVOXEL-BASED MULTI-SCALE POINT CLOUD SEGMENTATION USING FNEA FOR OBJECT-ORIENTED ROCK SLOPE CLASSIFICATION USING TLS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2020 (August 12, 2020): 1049–56. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2020-1049-2020.
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Abstract. Computer vision applications have been increasingly gaining space in the field of remote sensing and geosciences for automated terrain classification and semantic labelling purposes. The continuous and rapid development of monitoring techniques and enhancements in the spatial resolution of sensors have increased the demand for new remote sensing data analysis approaches. For semantic labelling of 2D (or 2.5D) image terrain representations for rock slopes, it has been shown that Object-Based Image Analysis (OBIA) results in high efficiency and accurate identification of landslide hazards. However, the application of such object-based approaches in 3D point cloud analysis is still under development for geospatial data analysis. In the field of engineering geology, which deals with complex rural landscapes, frequently the analysis needs to be conducted based solely on 3D geometrical information accounting for multiple scales simultaneously. In this study, the primary segmentation step of the object-based model is applied to a TLS-derived point cloud collected at a landslide-active rock slope. The 3D point cloud segmentation methodology proposed here builds on the principles of the Fractal Net Evolution Approach (FNEA). The objective is to provide a geometry-based point cloud segmentation framework that preserves the 3D character of the data throughout the process and favours the multi-scale analysis. The segmentation is performed on the basis of supervoxels based on purely geometrical local descriptors derived directly from the TLS point clouds and comprises the basis for the subsequent steps towards the development of an efficient Object-Based Point cloud Analysis (OBPA) framework in rock slope stability assessment by adding semantic meaning to the data through a homogenization process.
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Almac, Umut, Isıl Polat Pekmezci, and Metin Ahunbay. "Numerical Analysis of Historic Structural Elements Using 3D Point Cloud Data." Open Construction and Building Technology Journal 10, no. 1 (May 31, 2016): 233–45. http://dx.doi.org/10.2174/1874836801610010233.
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The 3D laser scanner has become a common instrument in numerous field applications such as structural health monitoring, assessment and documentation of structural damages, volume and dimension control of excavations, geometrical recording of built environment, and construction progress monitoring in different fields. It enables capture of millions of points from the surface of objects with high accuracy and in a very short time. These points can be employed to extrapolate the shape of the elements. In this way, the collected data can be developed to construct three-dimensional digital models that can be used in structural FEM analysis. This paper presents structural evaluation of a historic building through FE models with the help of a 3D point cloud. The main focus of the study is on the stone columns of a historic cistern. These deteriorated load bearing elements have severe non-uniform erosion, which leads to formation of significant stress concentrations. At this point, the 3D geometric data becomes crucial in revealing the stress distribution of severely eroded columns due to material deterioration. According to the results of static analysis using real geometry, maximum stress in compression increased remarkably on the columns in comparison with the geometrically idealized models. These values seem to approach the compressive strength of the material, which was obtained from the point load test results. Moreover, the stress distribution of the analysis draws attention to the section between columns and their capitals. According to the detailed 3D documentation, there is a reduced contact surface between columns and capitals to transfer loads.
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Antón, Daniel, and José-Lázaro Amaro-Mellado. "Engineering Graphics for Thermal Assessment: 3D Thermal Data Visualisation Based on Infrared Thermography, GIS and 3D Point Cloud Processing Software." Symmetry 13, no. 2 (February 18, 2021): 335. http://dx.doi.org/10.3390/sym13020335.
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Engineering graphics are present in the design stage, but also constitute a way to communicate, analyse, and synthesise. In the Architecture-Engineering-Construction sector, graphical data become essential in analysing buildings and constructions throughout their lifecycles, such as in the thermal behaviour assessment of building envelopes. Scientific research has addressed the thermal image mapping onto three-dimensional (3D) models for visualisation and analysis. However, the 3D point cloud data creation of buildings’ thermal behaviour directly from rectified infrared thermography (IRT) thermograms is yet to be investigated. Therefore, this paper develops an open-source software graphical method to produce 3D thermal data from IRT images for temperature visualisation and subsequent analysis. This low-cost approach uses both a geographic information system for the thermographic image rectification and the point clouds production, and 3D point cloud processing software. The methodology has been proven useful to obtain, without perspective distortions, 3D thermograms even from non-radiometric raster images. The results also revealed that non-rectangular thermograms enable over 95% of the 3D thermal data generated from IRT against rectangular shapes (over 85%). Finally, the 3D thermal data produced allow further thermal behaviour assessment, including calculating the object’s heat loss and thermal transmittance for diverse applications such as energy audits, restoration, monitoring, or product quality control.
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Lattanzio, A., F. Fell, R. Bennartz, I. F. Trigo, and J. Schulz. "Quality assessment and improvement of the EUMETSAT Meteosat Surface Albedo Climate Data Record." Atmospheric Measurement Techniques 8, no. 10 (October 30, 2015): 4561–71. http://dx.doi.org/10.5194/amt-8-4561-2015.
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Abstract. Surface albedo has been identified as an important parameter for understanding and quantifying the Earth's radiation budget. EUMETSAT generated the Meteosat Surface Albedo (MSA) Climate Data Record (CDR) currently comprising up to 24 years (1982–2006) of continuous surface albedo coverage for large areas of the Earth. This CDR has been created within the Sustained, Coordinated Processing of Environmental Satellite Data for Climate Monitoring (SCOPE-CM) framework. The long-term consistency of the MSA CDR is high and meets the Global Climate Observing System (GCOS) stability requirements for desert reference sites. The limitation in quality due to non-removed clouds by the embedded cloud screening procedure is the most relevant weakness in the retrieval process. A twofold strategy is applied to efficiently improve the cloud detection and removal. The first step consists of the application of a robust and reliable cloud mask, taking advantage of the information contained in the measurements of the infrared and visible bands. Due to the limited information available from old radiometers, some clouds can still remain undetected. A second step relies on a post-processing analysis of the albedo seasonal variation together with the usage of a background albedo map in order to detect and screen out such outliers. The usage of a reliable cloud mask has a double effect. It enhances the number of high-quality retrievals for tropical forest areas sensed under low view angles and removes the most frequently unrealistic retrievals on similar surfaces sensed under high view angles. As expected, the usage of a cloud mask has a negligible impact on desert areas where clear conditions dominate. The exploitation of the albedo seasonal variation for cloud removal has good potentialities but it needs to be carefully addressed. Nevertheless it is shown that the inclusion of cloud masking and removal strategy is a key point for the generation of the next MSA CDR release.
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Lattanzio, A., F. Fell, R. Bennartz, I. F. Trigo, and J. Schulz. "Quality assessment and improvement of the EUMETSAT Meteosat Surface Albedo Climate Data Record." Atmospheric Measurement Techniques Discussions 8, no. 7 (July 23, 2015): 7535–71. http://dx.doi.org/10.5194/amtd-8-7535-2015.
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Abstract. Surface albedo has been identified as an important parameter for understanding and quantifying the Earth's radiation budget. EUMETSAT generated the Meteosat Surface Albedo (MSA) Climate Data Record (CDR) currently comprising up to 24 years (1982–2006) of continuous surface albedo coverage for large areas of the Earth. This CDR has been created within the Sustained and Coordinated Processing of Environmental Satellite Data for Climate Monitoring (SCOPE-CM) framework. The long-term consistency of the MSA CDR is high and meets the Global Climate Observing System (GCOS) stability requirements for desert reference sites. The limitation in quality due to non removed clouds by the embedded cloud screening procedure is the most relevant weakness in the retrieval process. A twofold strategy is applied to efficiently improve the cloud detection and removal. A first step consists on the application of a robust and reliable cloud mask taking advantage of the information contained in the measurements of the infrared and visible bands. Due to the limited information available from old radiometers some clouds can still remain undetected. A second step relies on a post processing analysis of the albedo seasonal variation together with the usage of a background albedo map in order to detect and screen out such outliers. The usage of a reliable cloud mask has a double effect. It enhances the number of high quality retrievals for tropical forest areas sensed under low view angles and removes the most frequently unrealistic retrievals on similar surfaces sensed under high view angles. As expected, the usage of a cloud mask has a negligible impact on desert areas where clear conditions dominate. The exploitation of the albedo seasonal variation for cloud removal has good potentialities but it needs to be carefully addressed. Nevertheless it is shown that the inclusion of cloud masking and removal strategy is a key point for the generation of the next MSA CDR Release.
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Shlyahova, M. M., and I. Yu Lakeev. "Monitoring of oil and gas industry facilities using airborne laser scanning." Vestnik SSUGT (Siberian State University of Geosystems and Technologies) 27, no. 6 (2022): 64–72. http://dx.doi.org/10.33764/2411-1759-2022-27-6-64-72.
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The article presents a progressive method for performing aerial laser scanning of the territory of a well pad from an unmanned aerial vehicle DJI Matrice 300. The method includes the stages of planning, surveying, data processing and accuracy assessment. A description of the scanning process is given with a complete description of the experimental part and the main steps for processing the obtained data in the CoPre 2. By analyzing the profile of the point cloud, an accuracy assessment was made, which showed that the array of scanning points was obtained with an absolute accuracy not exceeding 0.10 and 0.05 m in terms of height, respectively, which, in turn, fit into the technical characteristics of the AlphaAir 450 mobile laser scanner declared by the manufacturer. The applicability of the described technique for using aerial laser scanning from an unmanned aircraft to study oil and gas industry objects is summed up.
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Arroyo-Mora, Juan Pablo, Margaret Kalacska, Alireza Roghani, and Oliver Lucanus. "Assessment of UAS Photogrammetry and Planet Imagery for Monitoring Water Levels around Railway Tracks." Drones 7, no. 9 (August 27, 2023): 553. http://dx.doi.org/10.3390/drones7090553.
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High water levels near railway tracks can be a major factor affecting the safety of train passage. Water conditions near the tracks are normally monitored through visual inspections. However, this method is limited in spatial coverage and may not provide comparable information over time. We evaluated the utility of satellite imagery (Planet Dove constellation at 3 m pixel size) at the landscape level to assess overall water surface area along railway tracks. Comparatively, we evaluated the use of Structure- from-Motion 3D point clouds and high spatial detail orthomosaics (3 cm) generated from a commercial off-the-shelf Unmanned Aerial System (UAS) (DJI M300 RTK) for measuring vertical water level changes and extent of surface water, respectively, within the right-of-way of a railway line in Ontario, Canada, in areas prone to high water level and flooding. Test sites of varied lengths (~180 m to 500 m), were assessed four times between June and October 2021. Our results indicate that the satellite imagery provides a large-scale overview regarding the extent of open water in wetlands at long distances from the railway tracks. Analysis of the UAS derived 3D point cloud indicates that changes in water level can be determined at the centimeter scale. Furthermore, the spatial error (horizontal and vertical alignments) between the multi-temporal UAS data collections between sites was less than 3 cm. Our research highlights the importance of using consistent UAS data collection protocols, and the significant potential of commercial off-the-shelf UAS systems for water level monitoring along railway tracks.
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Nikolova, Valentina, Veselina Gospodinova, and Asparuh Kamburov. "Assessment of Unmanned Aerial System Flight Plans for Data Acquisition from Erosional Terrain." Geosciences 14, no. 3 (March 12, 2024): 75. http://dx.doi.org/10.3390/geosciences14030075.
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Accurate data mapping and visualization are of crucial importance for the detection and monitoring of slope morphodynamics, including erosion processes and studying small erosional landforms (rills and gullies). The purpose of the current research is to examine how the flight geometry of unmanned aerial systems (UASs) could affect the accuracy of photogrammetric processing products, concerning small erosion landforms that are a result of slope wash and temporary small streams formed by rain. In October 2021, three UAS flights with a different geometry were carried out in a hilly to a low-mountain area with an average altitude of about 650 m where erosion processes are observed. UAS imagery processing was carried out using structure-from-motion (SfM) photogrammetry. High-resolution products such as photogrammetric-based point clouds, digital surface models (DSMs) and orthophotos were generated. The obtained data were compared and evaluated by the root mean square error (RMSE), length measurement, cloud-to-cloud comparison, and 3D spatial GIS analysis of DSMs. The results show small differences between the considered photogrammetric products generated by nadir-viewing and oblique-viewing (45°—single strip and 60°—cross strips) geometry. The complex analysis of the obtained photogrammetric products gives an advantage to the 60°—cross strips imagery, in studying erosional terrains with slow slope morphodynamics.
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Alsadik, Bashar, Nagham Amer Abdulateef, and Yousif Husain Khalaf. "Out of Plumb Assessment for Cylindrical-Like Minaret Structures Using Geometric Primitives Fitting." ISPRS International Journal of Geo-Information 8, no. 2 (January 29, 2019): 64. http://dx.doi.org/10.3390/ijgi8020064.
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Cultural heritage documentation and monitoring represents one of the major tasks for experts in the field of surveying, photogrammetry and geospatial engineering. Cultural heritage objects in countries like Iraq and Syria have suffered from intentional destruction or demolition during the last few years. Furthermore, many heritage sites in the mentioned places have an added religious value, and were either destroyed or are still in danger. Mosques, churches and shrines typically include one or multiple tower structures, and these towers or minarets are in many cases cylindrical-like objects. Because of their tall and relatively thin body, and adding in their age of construction, observing their inclination or out of plumb is of high importance. Accordingly, it is highly necessary for the continuous monitoring and assessment of their preservation and restoration. In this paper, we suggest an out of plumb assessment procedure using a geometric primitives least squares fitting technique, namely, cylinders, cones, and 3D circles. The approach is based on reconstructing a dense point cloud of the minaret tower which is scaled to reality by control points. Accordingly, the out of plumb is computed by fitting one of the mentioned 3D primitives to the minaret point cloud where its major axis orientation is computed. Two experimental tests of heritage objects in Iraq are presented: the lost heritage of the minaret al Hadbaa in the city of Mosul (1173 AD) and an existing inclined minaret of the religious shrine of Imam Musa AlKadhim in Baghdad (1058 AD). The results show the efficiency of the suggested methodology where the out of plumb is computed as 0.45m±1cm for the shrine minaret and 1.90m±10cm for the model of the minaret al Hadbaa.
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Murtiyoso, Arnadi, and Pierre Grussenmeyer. "Virtual Disassembling of Historical Edifices: Experiments and Assessments of an Automatic Approach for Classifying Multi-Scalar Point Clouds into Architectural Elements." Sensors 20, no. 8 (April 11, 2020): 2161. http://dx.doi.org/10.3390/s20082161.
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3D heritage documentation has seen a surge in the past decade due to developments in reality-based 3D recording techniques. Several methods such as photogrammetry and laser scanning are becoming ubiquitous amongst architects, archaeologists, surveyors, and conservators. The main result of these methods is a 3D representation of the object in the form of point clouds. However, a solely geometric point cloud is often insufficient for further analysis, monitoring, and model predicting of the heritage object. The semantic annotation of point clouds remains an interesting research topic since traditionally it requires manual labeling and therefore a lot of time and resources. This paper proposes an automated pipeline to segment and classify multi-scalar point clouds in the case of heritage object. This is done in order to perform multi-level segmentation from the scale of a historical neighborhood up until that of architectural elements, specifically pillars and beams. The proposed workflow involves an algorithmic approach in the form of a toolbox which includes various functions covering the semantic segmentation of large point clouds into smaller, more manageable and semantically labeled clusters. The first part of the workflow will explain the segmentation and semantic labeling of heritage complexes into individual buildings, while a second part will discuss the use of the same toolbox to segment the resulting buildings further into architectural elements. The toolbox was tested on several historical buildings and showed promising results. The ultimate intention of the project is to help the manual point cloud labeling, especially when confronted with the large training data requirements of machine learning-based algorithms.
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Terokhin, Vitalii L., Mykola G. Stervoyedov, and Oleg V. Ridozub. "Application Of The IoT Technology and Cloud Services for Radiation Monitoring." Control Systems and Computers, no. 2-3 (292-293) (July 2021): 60–68. http://dx.doi.org/10.15407/csc.2021.02.060.
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Introduction. Cloud services are the most promising technologies for monitoring radiation pollution. They are a set of geographically distributed wireless sensor nodes designed to collect, sometimes pre-process, information about environmental parameters, as well as to transmit this information to remote users. Purpose. Development of basic methods for applying cloud services for IoT radiation and Environmental Research Technology. A comprehensive assessment of the state of the ecosystem, including its impact on humans, was carried out. At the same time, a promising direction is proposed, namely the integration of on-premises measuring devices with cloud services using M2M/IoT technology for remote measurement, the use of promising semiconductor sensors based on CdTe and CdZnTe radiation detectors, and modern microcontrollers. Methods. Use of methods Wi-Fi access point, control of the sensor network via a smartphone to transfer data parameters to the ThingSpeak cloud service. Results. When writing approaches to building cloud services, the composition of each sensor node and the task that it performs are considered, such as: the type of data collected, location, power sources, and the possibility of using certain protocols for data exchange relationships. Conclusion. The analysis of unified cloud services that include methods of designing information and measurement systems, methods of building machine-machine and human-machine interfaces, methods of designing sensor networks, methods of computer modeling of electronic circuits and systems, hardware emulation method (based on QEMU), methods of analysis, system analysis, synthesis, logical generalization of results. It includes selecting and connecting layout hardware, data module software that is developed in the ThingSpeak environment using the HTML markup language to describe the configuration web page.
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Mistretta, Fausto, Giannina Sanna, Flavio Stochino, and Giuseppina Vacca. "Structure from Motion Point Clouds for Structural Monitoring." Remote Sensing 11, no. 16 (August 20, 2019): 1940. http://dx.doi.org/10.3390/rs11161940.
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Dense point clouds acquired from Terrestrial Laser Scanners (TLS) have proved to be effective for structural deformation assessment. In the last decade, many researchers have defined methodology and workflow in order to compare different point clouds, with respect to each other or to a known model, assessing the potentialities and limits of this technique. Currently, dense point clouds can be obtained by Close-Range Photogrammetry (CRP) based on a Structure from Motion (SfM) algorithm. This work reports on a comparison between the TLS technique and the Close-Range Photogrammetry using the Structure from Motion algorithm. The analysis of two Reinforced Concrete (RC) beams tested under four-points bending loading is presented. In order to measure displacement distributions, point clouds at different beam loading states were acquired and compared. A description of the instrumentation used and the experimental environment, along with a comprehensive report on the calculations and results obtained is reported. Two kinds of point clouds comparison were investigated: Mesh to mesh and modeling with geometric primitives. The comparison between the mesh to mesh (m2m) approach and the modeling (m) one showed that the latter leads to significantly better results for both TLS and CRP. The results obtained with the TLS for both m2m and m methodologies present a Root Mean Square (RMS) levels below 1 mm, while the CRP method yields to an RMS level of a few millimeters for m2m, and of 1 mm for m.
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Arunajyothi, G. "Key based Access Control Policies to Solve Security in Cloud Data Sharing." International Journal of Emerging Research in Management and Technology 6, no. 12 (June 11, 2018): 45. http://dx.doi.org/10.23956/ijermt.v6i12.33.
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Cloud achieves not just diverse levels of accommodation and proficiency issues albeit persistently advancing in such manner, additionally extraordinary difficulties in the field of information assurance. SaaS based distributed computing stockpiling suppliers, for example, google, send space have been there for calm at some point with the security viewpoint continually being disregarded. So we propose a cloud construction modeling that addresses the security perspective as for encryption, access control and with respect to risk assessment, key controls, and monitoring and reporting execution sign. We additionally broaden it with data transmission estimation plan which is another key execution pointer of mists. Contrasted with before methodologies this plan has a lesser calculation overhead and is considered cutting edge because of usage of every conceivable execution parameter of cloud area.
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Jarahizadeh, Sina, and Bahram Salehi. "A Comparative Analysis of UAV Photogrammetric Software Performance for Forest 3D Modeling: A Case Study Using AgiSoft Photoscan, PIX4DMapper, and DJI Terra." Sensors 24, no. 1 (January 3, 2024): 286. http://dx.doi.org/10.3390/s24010286.
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Three-dimensional (3D) modeling of trees has many applications in various areas, such as forest and urban planning, forest health monitoring, and carbon sequestration, to name a few. Unmanned Aerial Vehicle (UAV) photogrammetry has recently emerged as a low cost, rapid, and accurate method for 3D modeling of urban and forest trees replacing the costly traditional methods such as plot measurements and surveying. There are numerous commercial and open-source software programs available, each processing UAV data differently to generate forest 3D modeling and photogrammetric products, including point clouds, Digital Surface Models (DSMs), Canopy Height Models (CHMs), and orthophotos in forest areas. The objective of this study is to compare the three widely-used commercial software packages, namely, AgiSoft Photoscan (Metashape) V 1.7.3, PIX4DMapper (Pix4D) V 4.4.12, and DJI Terra V 3.7.6 for processing UAV data over forest areas from three perspectives: point cloud density and reconstruction quality, computational time, DSM assessment for height accuracy (z) and ability of tree detection on DSM. Three datasets, captured by UAVs on the same day at three different flight altitudes, were used in this study. The first, second, and third datasets were collected at altitudes of 60 m, 100 m, and 120 m, respectively over a forested area in Tully, New York. While the first and third datasets were taken horizontally, the second dataset was taken 20 degrees off-nadir to investigate the impact of oblique images. Results show that Pix4D and AgiSoft generate 2.5 times denser point clouds than DJI Terra. However, reconstruction quality evaluation using the Iterative Closest Point method (ICP) shows DJI Terra has fewer gaps in the point cloud and performed better than AgiSoft and Pix4D in generating a point cloud of trees, power lines and poles despite producing a fewer number of points. In other words, the outperformance in key points detection and an improved matching algorithm are key factors in generating improved final products. The computational time comparison demonstrates that the processing time for AgiSoft and DJI Terra is roughly half that of Pix4D. Furthermore, DSM elevation profiles demonstrate that the estimated height variations between the three software range from 0.5 m to 2.5 m. DJI Terra’s estimated heights are generally greater than those of AgiSoft and Pix4D. Furthermore, DJI Terra outperforms AgiSoft and Pix4D for modeling the height contour of trees, buildings, and power lines and poles, followed by AgiSoft and Pix4D. Finally, in terms of the ability of tree detection, DJI Terra outperforms AgiSoft and Pix4D in generating a comprehensive DSM as a result of fewer gaps in the point cloud. Consequently, it stands out as the preferred choice for tree detection applications. The results of this paper can help 3D model users to have confidence in the reliability of the generated 3D models by comprehending the accuracy of the employed software.
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Mestre-Runge, Christian, Jorge Lorenzo-Lacruz, Aaron Ortega-Mclear, and Celso Garcia. "An Optimized Workflow for Digital Surface Model Series Generation Based on Historical Aerial Images: Testing and Quality Assessment in the Beach-Dune System of Sa Ràpita-Es Trenc (Mallorca, Spain)." Remote Sensing 15, no. 8 (April 12, 2023): 2044. http://dx.doi.org/10.3390/rs15082044.
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We propose an optimized Structure-from-Motion (SfM) Multi-View Stereopsis (MVS) workflow, based on minimizing different errors and inaccuracies of historical aerial photograph series (1945, 1979, 1984, and 2008 surveys), prior to generation of elevation-calibrated historical Digital Surface Models (hDSM) at 1 m resolution. We applied LiDAR techniques on Airborne Laser Scanning (ALS) point clouds (Spanish PNOA LiDAR flights of 2014 and 2019) for comparison and validation purposes. Implementation of these products in multi-temporal analysis requires quality control due to the diversity of sources and technologies involved. To accomplish this, (i) we used the Mean Absolute Error (MAE) between GNSS-Validation Points and the elevations observed by DSM-ALS to evaluate the elevation accuracy of DSM-ALS generated with the LAScatalog processing engine; (ii) optimization of the SfM sparse clouds in the georeferencing step was evaluated by calculating the Root Mean Square Error (RMSE) between the Check Points extracted from DSM-ALS and the predicted elevations per sparse cloud; (iii) the MVS clouds were evaluated by calculating the MAE between ALS-Validation Points and the predicted elevations per MVS cloud; iv) the accuracy of the resulting historical SfM-MVS DSMs were assessed using the MAE between ALS-Validation Points and the observed elevations per historical DSM; and (v) we implemented a calibration method based on a linear correction to reduce the elevation discrepancies between historical DSMs and the DSM-ALS 2019 reference elevations. This optimized workflow can generate high-resolution (1 m pixel size) hDSMs with reasonable accuracy: MAE in z ranges from 0.41 m (2008 DSM) to 5.21 m (1945 DSM). Overall, hDSMs generated using historical images have great potential for geo-environmental processes monitoring in different ecosystems and, in some cases (i.e., sufficient image overlapping and quality), being an acceptable replacement for LiDAR data when it is not available.
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Taheriazad, Leila, Hamid Moghadas, and Arturo Sanchez Azofeifa. "Automatic Separation of Photosynthetic Components in a LiDAR Point Cloud Data Collected from a Canadian Boreal Forest." Forests 15, no. 1 (December 29, 2023): 70. http://dx.doi.org/10.3390/f15010070.
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Terrestrial LiDAR has emerged as a promising technology for accurate forest assessment. LiDAR can provide a 3D image composed of a cloud of points using a rotary laser scanner. The point cloud data (PCD) contain information on the (x, y, z) coordinates of every single scanned point and a raw intensity parameter. This study introduces an algorithm for the automatic and accurate separation of the photosynthetic features of a PCD. It is shown that the recorded raw intensity is not a suitable parameter for the separation of photosynthetic features. Instead, for the first time, the absorption intensity is developed for every point based on its raw intensity and distance from the scanner, using proper scaling functions. Then, the absorption intensity is utilized as the only criterion for the classification of the points between photosynthetic and non-photosynthetic features. The proposed method is applied to the scans from a Canadian Boreal Forest and successfully extracted the photosynthetic features with minimal average type I and type II error rates of 5.7% and 4.8%. The extracted photosynthetic PCD can be readily used for calculating important forest parameters such as the leaf area index (LAI) and the green biomass. In addition, it can be used for estimating forest carbon storage and monitoring temporal changes in vegetation structure and ecosystem health.
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Panwar, Arjun. "DEVELOPING AN ADVANCED MACHINE LEARNING AND INTERNET OF THINGS (IOT) BASED SYSTEM TO DEVISE AN EFFECTIVE HEALTHCARE MONITORING SYSTEM." INTERNATIONAL JOURNAL OF RESEARCH IN MEDICAL SCIENCES & TECHNOLOGY 12, no. 01 (2022): 228–37. http://dx.doi.org/10.37648/ijrmst.v11i02.019.
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An absence of health insurance every day likewise disguises medical problems. These issues often establish a danger to public wellbeing, which is generally ignored until past the point of no return. Subsequently, we have encouraged many standards to address and tackle the previously-mentioned issues. We constantly screen the essential organs in our framework impart information to cloud-based specialists, and ready patients for likely risks. We have designed and IOT Based framework that links various sensors to a microcomputer and stores data on to cloud server for SGD algorithm with a deep learning combination. If the specialist thinks of a medical condition, they might give a warning using our gadget in the wake of finishing the assessment. Our proposed approach works for Health Monitoring in IoT frameworks.
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Riveiro, B., M. DeJong, and B. Conde. "AN AUTOMATIC METHOD FOR GEOMETRIC SEGMENTATION OF MASONRY ARCH BRIDGES FOR STRUCTURAL ENGINEERING PURPOSES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B5 (June 16, 2016): 719–24. http://dx.doi.org/10.5194/isprs-archives-xli-b5-719-2016.
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Despite the tremendous advantages of the laser scanning technology for the geometric characterization of built constructions, there are important limitations preventing more widespread implementation in the structural engineering domain. Even though the technology provides extensive and accurate information to perform structural assessment and health monitoring, many people are resistant to the technology due to the processing times involved. Thus, new methods that can automatically process LiDAR data and subsequently provide an automatic and organized interpretation are required. <br><br> This paper presents a new method for fully automated point cloud segmentation of masonry arch bridges. The method efficiently creates segmented, spatially related and organized point clouds, which each contain the relevant geometric data for a particular component (pier, arch, spandrel wall, etc.) of the structure. The segmentation procedure comprises a heuristic approach for the separation of different vertical walls, and later image processing tools adapted to voxel structures allows the efficient segmentation of the main structural elements of the bridge. The proposed methodology provides the essential processed data required for structural assessment of masonry arch bridges based on geometric anomalies. The method is validated using a representative sample of masonry arch bridges in Spain.
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Bassier, Maarten, Stan Vincke, Heinder De Winter, and Maarten Vergauwen. "Drift Invariant Metric Quality Control of Construction Sites Using BIM and Point Cloud Data." ISPRS International Journal of Geo-Information 9, no. 9 (September 14, 2020): 545. http://dx.doi.org/10.3390/ijgi9090545.
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Construction site monitoring is currently performed through visual inspections and costly selective measurements. Due to the small overhead in construction projects, additional resources are scarce to frequently conduct a metric quality assessment of the constructed objects. However, contradictory, construction projects are characterised by high failure costs which are often caused by erroneously constructed structural objects. With the upcoming use of periodic remote sensing during the different phases of the building process, new possibilities arise to advance from a selective quality analysis to an in-depth assessment of the full construction site. In this work, a novel methodology is presented to rapidly evaluate a large number of built objects on a construction site. Given a point cloud and a set of as-design BIM elements, our method evaluates the deviations between both datasets and computes the positioning errors of each object. Unlike the current state of the art, our method computes the error vectors regardless of drift, noise, clutter and (geo)referencing errors, leading to a better detection rate. The main contributions are the efficient matching of both datasets, the drift invariant metric evaluation and the intuitive visualisation of the results. The proposed analysis facilitates the identification of construction errors early on in the process, hence significantly lowering the failure costs. The application is embedded in native BIM software and visualises the objects by a simple color code, providing an intuitive indicator for the positioning accuracy of the built objects.
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Jo, Hyeon Cheol, Hong-Gyoo Sohn, and Yun Mook Lim. "A LiDAR Point Cloud Data-Based Method for Evaluating Strain on a Curved Steel Plate Subjected to Lateral Pressure." Sensors 20, no. 3 (January 28, 2020): 721. http://dx.doi.org/10.3390/s20030721.
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Structural health monitoring (SHM) and safety assessment are very important areas for evaluating the behavior of structures. Various wired and wireless sensors can measure the physical responses of structures, such as displacement or strain. One recently developed wireless technique is a light imaging detection and ranging (LiDAR) system that can remotely acquire three-dimensional (3D) high-precision coordinate information using 3D laser scanning. LiDAR systems have been previously used in geographic information systems (GIS) to collect information on geography and terrain. Recently, however, LiDAR is used in the SHM field to analyze structural behavior, as it can remotely detect the surface and deformation shape of structures without the need for attached sensors. This study demonstrates a strain evaluation method using a LiDAR system in order to analyze the behavior of steel structures. To evaluate the strains of structures from the initial and deformed shape, a combination of distributed 3D point cloud data and finite element methods (FEM) was used. The distributed 3D point cloud data were reconstructed into a 3D mesh model, and strains were calculated using the FEM. By using the proposed method, the strain could be calculated at any point on a structure for SHM and safety assessment during construction.
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Shin, D. Y., J. S. Sim, and K. S. Lee. "APPLICATION OF THE STEEP SLOPE RISK ASSESSMENT USING THREE DIMENSIONAL INFORMATION DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W8 (August 22, 2019): 381–86. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w8-381-2019.
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<p><strong>Abstract.</strong> A collapse of slope is one of the natural disasters that often occur during the early spring and the rainy season. In order to prevent this kind of disaster, safety monitoring is carried out through risk assessment. This assessment consists of various parameters such as inclination angle and height of the slope, and inspectors evaluate the score using the compass, the laser range finder, and so on. This approach is, however, consumed a lot of the manpower and the time. This study, therefore, aims to evaluate the rapid and accurate steep slope risk by using a terrestrial LiDAR which takes 3 dimensional spatial information data. 3D spatial information data was acquired using the terrestrial LiDAR for steep slopes classified as very unstable slopes. Noise and vegetation of the acquired scan data were removed to generate point cloud data with a rock or mountain model without vegetation. The RMSE of the registration accuracy was 0.0156 m. From the point cloud data, the inclination angle, height, shape, valley, collapse and loss were evaluated. As a result, various risk assessment parameters can be checked at once. In addition, it is expected to be used as basic data for constructing steep slope DB, providing visualization data, and time series analysis in the future.</p>
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Shrestha, Abhinav, Jeffrey A. Hicke, Arjan J. H. Meddens, Jason W. Karl, and Amanda T. Stahl. "Evaluating a Novel Approach to Detect the Vertical Structure of Insect Damage in Trees Using Multispectral and Three-Dimensional Data from Drone Imagery in the Northern Rocky Mountains, USA." Remote Sensing 16, no. 8 (April 12, 2024): 1365. http://dx.doi.org/10.3390/rs16081365.
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Remote sensing is a well-established tool for detecting forest disturbances. The increased availability of uncrewed aerial systems (drones) and advances in computer algorithms have prompted numerous studies of forest insects using drones. To date, most studies have used height information from three-dimensional (3D) point clouds to segment individual trees and two-dimensional multispectral images to identify tree damage. Here, we describe a novel approach to classifying the multispectral reflectances assigned to the 3D point cloud into damaged and healthy classes, retaining the height information for the assessment of the vertical distribution of damage within a tree. Drone images were acquired in a 27-ha study area in the Northern Rocky Mountains that experienced recent damage from insects and then processed to produce a point cloud. Using the multispectral data assigned to the points on the point cloud (based on depth maps from individual multispectral images), a random forest (RF) classification model was developed, which had an overall accuracy (OA) of 98.6%, and when applied across the study area, it classified 77.0% of the points with probabilities greater than 75.0%. Based on the classified points and segmented trees, we developed and evaluated algorithms to separate healthy from damaged trees. For damaged trees, we identified the damage severity of each tree based on the percentages of red and gray points and identified top-kill based on the length of continuous damage from the treetop. Healthy and damaged trees were separated with a high accuracy (OA: 93.5%). The remaining damaged trees were separated into different damage severities with moderate accuracy (OA: 70.1%), consistent with the accuracies reported in similar studies. A subsequent algorithm identified top-kill on damaged trees with a high accuracy (OA: 91.8%). The damage severity algorithm classified most trees in the study area as healthy (78.3%), and most of the damaged trees in the study area exhibited some amount of top-kill (78.9%). Aggregating tree-level damage metrics to 30 m grid cells revealed several hot spots of damage and severe top-kill across the study area, illustrating the potential of this methodology to integrate with data products from space-based remote sensing platforms such as Landsat. Our results demonstrate the utility of drone-collected data for monitoring the vertical structure of tree damage from forest insects and diseases.
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Abreu, Nuno, Andry Pinto, Aníbal Matos, and Miguel Pires. "Procedural Point Cloud Modelling in Scan-to-BIM and Scan-vs-BIM Applications: A Review." ISPRS International Journal of Geo-Information 12, no. 7 (June 30, 2023): 260. http://dx.doi.org/10.3390/ijgi12070260.
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Point cloud processing is an essential task in many applications in the AEC domain, such as automated progress assessment, quality control and 3D reconstruction. As much of the procedure used to process the point clouds is shared among these applications, we identify common processing steps and analyse relevant algorithms found in the literature published in the last 5 years. We start by describing current efforts on both progress and quality monitoring and their particular requirements. Then, in the context of those applications, we dive into the specific procedures related to processing point clouds acquired using laser scanners. An emphasis is given to the scan planning process, as it can greatly influence the data collection process and the quality of the data. The data collection phase is discussed, focusing on point cloud data acquired by laser scanning. Its operating mode is explained and the factors that influence its performance are detailed. Data preprocessing methodologies are presented, aiming to introduce techniques used in the literature to, among other aspects, increase the registration performance by identifying and removing redundant data. Geometry extraction techniques are described, concerning both interior and outdoor reconstruction, as well as currently used relationship representation structures. In the end, we identify certain gaps in the literature that may constitute interesting topics for future research. Based on this review, it is evident that a key limitation associated with both Scan-to-BIM and Scan-vs-BIM algorithms is handling missing data due to occlusion, which can be reduced by multi-platform sensor fusion and efficient scan planning. Another limitation is the lack of consideration for laser scanner performance characteristics when planning the scanning operation and the apparent disconnection between the planning and data collection stages. Furthermore, the lack of representative benchmark datasets is hindering proper comparison of Scan-to-BIM and Scan-vs-BIM techniques, as well as the integration of state-of-the-art deep-learning methods that can give a positive contribution in scene interpretation and modelling.
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Scaioni, M., J. Crippa, V. Yordanov, L. Longoni, V. I. Ivanov, and M. Papini. "SOME TOOLS TO SUPPORT TEACHING PHOTOGRAMMETRY FOR SLOPE STABILITY ASSESSMENT AND MONITORING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W4 (March 6, 2018): 453–60. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w4-453-2018.
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<p><strong>Abstract.</strong> This paper describes the use of some tool to help training of photogrammetry for applications in the field of landslide and slope stability assessment and monitoring. These tools have been used in classes of the MSc on Civil Eng. for Risk Mitigation at Politecnico di Milano university, Lecco (Italy). The first tools are hardware facilities. The first one consists of a ‘Landslide Simulator,’ where shallow landslides may be reproduced at small scale. Simulations are also used here for active-learning purpose. In particular, here the use of digital images to obtain multi-temporal information is presented. The second tool is a ‘Rock face 3D Modelling Simulator.’ This is used by students to learn how a photogrammetric block should be designed in order to reconstruct rock slopes using Structure-from-Motion photogrammetry. The last to tools are software packages (CloudCompare and LIME) devoted to point cloud analysis (including change detection/ deformation analysis) and advanced visualization, respectively. The combination of these tools together with datasets from either lab and the real field, has been successfully tested to provide efficient training to students in an active-learning fashion.</p>
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Manish, Raja, Seyyed Meghdad Hasheminasab, Jidong Liu, Yerassyl Koshan, Justin Anthony Mahlberg, Yi-Chun Lin, Radhika Ravi, et al. "Image-Aided LiDAR Mapping Platform and Data Processing Strategy for Stockpile Volume Estimation." Remote Sensing 14, no. 1 (January 5, 2022): 231. http://dx.doi.org/10.3390/rs14010231.
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Stockpile quantity monitoring is vital for agencies and businesses to maintain inventory of bulk material such as salt, sand, aggregate, lime, and many other materials commonly used in agriculture, highways, and industrial applications. Traditional approaches for volumetric assessment of bulk material stockpiles, e.g., truckload counting, are inaccurate and prone to cumulative errors over long time. Modern aerial and terrestrial remote sensing platforms equipped with camera and/or light detection and ranging (LiDAR) units have been increasingly popular for conducting high-fidelity geometric measurements. Current use of these sensing technologies for stockpile volume estimation is impacted by environmental conditions such as lack of global navigation satellite system (GNSS) signals, poor lighting, and/or featureless surfaces. This study addresses these limitations through a new mapping platform denoted as Stockpile Monitoring and Reporting Technology (SMART), which is designed and integrated as a time-efficient, cost-effective stockpile monitoring solution. The novel mapping framework is realized through camera and LiDAR data-fusion that facilitates stockpile volume estimation in challenging environmental conditions. LiDAR point clouds are derived through a sequence of data collections from different scans. In order to handle the sparse nature of the collected data at a given scan, an automated image-aided LiDAR coarse registration technique is developed followed by a new segmentation approach to derive features, which are used for fine registration. The resulting 3D point cloud is subsequently used for accurate volume estimation. Field surveys were conducted on stockpiles of varying size and shape complexity. Independent assessment of stockpile volume using terrestrial laser scanners (TLS) shows that the developed framework had close to 1% relative error.
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Zaforemska, A., R. Gaulton, J. Mills, and W. Xiao. "EVALUATION OF A LOW-COST PHOTOGRAMMETRIC SYSTEM FOR THE RETRIEVAL OF 3D TREE ARCHITECTURE." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W2-2023 (December 13, 2023): 1097–104. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w2-2023-1097-2023.
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Abstract. Reconstruction of major branches of a tree is an important first step for the monitoring of tree sway and assessment of structural stability. Photogrammetric systems can provide a low-cost alternative for the acquisition of three-dimensional data, while also enabling long-term monitoring of a tree of interest. This study introduces a low-cost photogrammetric system based on two Raspberry Pi cameras, which is used to reconstruct the tree architecture for the purpose of stability monitoring. Images of five trees are taken at a range of distances and the resulting point clouds are evaluated in terms of point density and distribution with the reference to TLS. While the photogrammetric point clouds are sparse, it was found that they are capable of reconstructing the tree trunk and lower-order branches, which are most relevant for sway monitoring and tree stability assessment. The most optimal distance for the reconstruction of the relevant branches was found to be 9–10 m, with a baseline of 120 cm.
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Riveiro, B., M. DeJong, and B. Conde. "AN AUTOMATIC METHOD FOR GEOMETRIC SEGMENTATION OF MASONRY ARCH BRIDGES FOR STRUCTURAL ENGINEERING PURPOSES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B5 (June 16, 2016): 719–24. http://dx.doi.org/10.5194/isprsarchives-xli-b5-719-2016.
Full textAbstract:
Despite the tremendous advantages of the laser scanning technology for the geometric characterization of built constructions, there are important limitations preventing more widespread implementation in the structural engineering domain. Even though the technology provides extensive and accurate information to perform structural assessment and health monitoring, many people are resistant to the technology due to the processing times involved. Thus, new methods that can automatically process LiDAR data and subsequently provide an automatic and organized interpretation are required. <br><br> This paper presents a new method for fully automated point cloud segmentation of masonry arch bridges. The method efficiently creates segmented, spatially related and organized point clouds, which each contain the relevant geometric data for a particular component (pier, arch, spandrel wall, etc.) of the structure. The segmentation procedure comprises a heuristic approach for the separation of different vertical walls, and later image processing tools adapted to voxel structures allows the efficient segmentation of the main structural elements of the bridge. The proposed methodology provides the essential processed data required for structural assessment of masonry arch bridges based on geometric anomalies. The method is validated using a representative sample of masonry arch bridges in Spain.