Triangulated Irregular Network Research Articles

Impact of Hurricane Irma on coral reef sediment redistribution at Looe Key Reef, Florida, USA

Abstract. Understanding event-driven sediment transport in coral reef environments is essential to assessing impacts on reef species, habitats, restoration, and mitigation, yet a global knowledge gap remains due to limited quantitative studies. Hurricane Irma made landfall in the Lower Florida Keys with sustained 209 km h−1 winds and waves greater than 8 m on 10 September 2017, directly impacting the Florida Reef Tract (FRT) and providing an opportunity to perform a unique comprehensive, quantitative assessment of its impact on coral reef structure and sediment redistribution. We used lidar and multibeam derived digital elevation models (DEMs) collected before and after the passing of Hurricane Irma over a 15.98 km2 area along the lower FRT including Looe Key Reef to quantify changes in seafloor elevation, volume, and structure due to storm impacts. Elevation change was calculated at over 4 million point locations across 10 habitat types within this study area for two time periods using data collected (1) approximately 1 year before the passing of Irma and 3 to 6 months following the storm's impact as well as (2) 3 to 6 months after and up to 16.5 months after the storm. Elevation change data were then used to generate triangulated irregular network (TIN) models in ArcMap to calculate changes in seafloor volume during each time period. Our results indicate that Hurricane Irma was primarily a depositional event that increased mean seafloor elevation and volume at this study site by 0.34 m and up to 5.4 Mm3, respectively. Sediment was transported primarily west-southwest (WSW) and downslope, modifying geomorphic seafloor features including the migration of sand waves and rubble fields, formation of scour marks in shallow seagrass habitats, and burial of seagrass and coral-dominated habitats. Approximately 16.5 months after Hurricane Irma (during a 13-month period between 2017 and 2019), net erosion was observed across all habitats with mean elevation change of −0.15 m and net volume change up to −2.46 Mm3. Rates of elevation change during this post-storm period were 1 to 2 orders of magnitude greater than decadal and multi-decadal rates of change in the same location, and changes showed erosion of approximately 50 % of sediment deposited during the storm event as seafloor sediment distribution began to re-equilibrate to non-storm sea-state conditions. Our results suggest that higher-resolution elevation change data collected over seasonal and annual time periods could enhance characterization and understanding of short-term and long-term rates and processes of seafloor change.

Parallel Topology-aware Mesh Simplification on Terrain Trees

We address the problem of performing a topology-aware simplification algorithm on a compact and distributed data structure for triangle meshes, the Terrain trees. Topology-aware operators have been defined to coarsen a Triangulated Irregular Network (TIN) without affecting the topology of its underlying terrain, i.e., without modifying critical features of the terrain, such as pits, saddles, peaks, and their connectivity. However, their scalability is limited for large-scale meshes. Our proposed algorithm uses a batched processing strategy to reduce both the memory and time requirements of the simplification process, and thanks to the spatial decomposition on the basis of Terrain trees, it can be easily parallelized. Also, since a Terrain tree after the simplification process becomes less compact and efficient, we propose an efficient post-processing step for updating hierarchical spatial decomposition. Our experiments on real-world TINs, derived from topographic and bathymetric LiDAR data, demonstrate the scalability and efficiency of our approach. Specifically, topology-aware simplification on Terrain trees uses 40% less memory and half the time compared to the most compact and efficient connectivity-based data structure for TINs. Furthermore, the parallel simplification algorithm on the Terrain trees exhibits a 12× speedup with an OpenMP implementation. The quality of the output mesh is not significantly affected by the distributed and parallel simplification strategy of Terrain trees, and we obtain similar quality levels compared to the global baseline method.

Evaluating the performance of various interpolation techniques on digital elevation models in highly dense forest vegetation environment

A Digital Elevation Model (DEM) is a numerical representation of the Earth's topography using an array of cells or pixels. DEMs are important for various purposes such as hydrological studies and environmental monitoring in situations where there is a need to assess land use changes, deforestation, and disaster response. In this study, various spatial interpolation methods, such as the Inverse distance weighting (IDW) technique, Natural Neighbor, and Triangulated Irregular Network (TIN) methods, were compared to evaluate the performance in generating accurate and reliable DEMs. We also considered factors such as computational efficiency and ease of implementation to determine the most suitable method. The precision of the generated DEMs was assessed through metrics such as root mean square error (RMSE) and mean absolute bias (MAB). The predicted elevation models were compared with elevation values obtained through ground surveys. Furthermore, a raster agreement analysis was used to compare the elevation values recorded by the two data sources, the USGS 3D Elevation Program (USGS 3DEP) and Unmanned Aerial Vehicle (UAV). The analysis revealed that TIN method demonstrated the highest level of agreement at 92%, while the binning technique showed inconsistency in predicting the elevation values based on the data source. In regions with high forest cover, the results were less accurate due to the limited number of last returns available for developing the DEM. This study also found that TIN recorded the fastest processing time per square acre. The presence of vegetation significantly influences the quantity of Light Detection and Ranging (LiDAR) pulses capable of reaching the forest floor, subsequently impacting the quantity of points available for the creation of a DEM.

A Study of Precipitation Forecasting for the Pre-Summer Rainy Season in South China Based on a Back-Propagation Neural Network

In South China, the large quantity of rainfall in the pre-summer rainy season can easily lead to natural disasters, which emphasizes the importance of improving the accuracy of precipitation forecasting during this period for the social and economic development of the region. In this paper, the back-propagation neural network (BPNN) is used to establish the model for precipitation forecasting. Three schemes are applied to improve the model performance: (1) predictors are selected based on individual meteorological stations within the region rather than the region as a whole; (2) the triangular irregular network (TIN) is proposed to preprocess the observed precipitation data for input of the BPNN model, while simulated/forecast precipitation is the expected output; and (3) a genetic algorithm is used for the hyperparameter optimization of the BPNN. The first scheme reduces the mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the simulation by roughly 5% and more than 15 mm; the second reduces the MAPE and RMSE by more than 15% and 15 mm, respectively, while the third improves the simulation inapparently. Obviously, the second scheme raises the upper limit of the model simulation capability greatly by preprocessing the precipitation data. During the training and validation periods, the MAPE of the improved model can be controlled at approximately 35%. For precipitation hindcasting in the test period, the anomaly rate is less than 50% in only one season, and the highest is 64.5%. According to the anomaly correlation coefficient and Ps score of the hindcast precipitation, the improved model performance is slightly better than the FGOALS-f2 model. Although global climate change makes the predictors more variable, the trend of simulation is almost identical to that of the observed values over the whole period, suggesting that the model is able to capture the general characteristics of climate change.

A stream-aligned mixed polyhedral meshing strategy for integrated surface-subsurface hydrological models

Mesh refinement near river corridors is commonly deployed to resolve topographic details important for accurately representing riverine flow dynamics in watershed-scale spatially distributed integrated hydrology models. However, this increased accuracy comes with higher computational costs due to larger mesh and smaller time steps resulting from small mesh cells. We present a novel stream-aligned mixed-polygonal meshing approach that resolves river-corridor topography efficiently by placing long quadrilateral cells along the mapped center lines of the rivers and standard Triangulated Irregular Network (TIN) mesh in the rest of the domain. Our integrated hydrology simulations using mixed-polyhedral mesh generated from the mixed-polygonal approach closely match those using a highly refined extruded-TIN mesh but with a 96.43% reduction in the number of mesh cells and a 99.75% reduction in CPU hours. Conversely, coarser extruded-TIN meshes, due to poor water conveyance relative to the refined mesh simulations, yielded lower peak flows, longer recession curves, and higher inundation fractions.The creation of this mixed-polyhedral mesh is implemented within the Watershed Workflow tool, a Python-based workflow library for watershed simulation. We also implemented a layered process of hydrologic conditioning specific to the cells in the river corridor, which are explicitly meshed within the mixed-polyhedral mesh. The hydrologic conditioning enforces monotonic topographic gradients, removes spurious obstructions, and allows for burning-in depths of the river cells based on ancillary information. This approach paves the way for efficiently modeling narrow engineered channels and specifying river-specific hydrodynamic details and biogeochemical processes.

PERFORMANCE ASSESSMENT OF SPATIAL INTERPOLATIONS FOR TRAFFIC NOISE MAPPING ON UNDULATING AND LEVEL TERRAIN

Traffic noise mapping frequently employs Kriging, Inverse Distance Weighted (IDW), and Triangular Irregular Networks (TIN) spatial interpolations. This study uses the Henk de Kluijver noise model to evaluate the performance of spatial interpolations. Effective traffic noise mapping requires that noise observation points (Nops) be designed as 2 m grids. The upper and lower slopes function as noise barriers to reduce sound levels. Therefore, assessment of accuracy is essential for visualising noise levels in undulating and level terrain. In addition, this work gives an accurate comparison of traffic noise interpolation in undulating areas. The elements of spatial interpolations, such as the weighting factor, variogram, radius, and number of points influence the interpolation accuracy. The Kriging with a Gaussian variogram, where the radius is 5 m and the number of points is 12 demonstrates the highest level of precision. However, there is no direct relationship between accuracy validation and cross-validation. In cross-validation, however, the accuracy of the Gaussian variogram with a 7 m radius and 18 points is more accurate. In addition, this study demonstrates that Kriging is superior for extrapolating noise levels in undulating regions. Accurate visualising traffic noise levels requires a prior understanding of spatial interpolations.

Nanoparticles in terrestrial sediments and the behavior of the spectral optics of Sentinel-3B OLCI Satellite images in a river basin of UNESCO World Cultural and Natural Heritage.

The dangerous chemical elements associated with nanoparticles (NPs) and ultra-fine sediment particles in hydrological bays have the capacity to move contaminants to large oceanic regions. The general objective of this study is to quantify the major chemical elements present in NPs and ultra-fine particles in aquatic sediments sampled from Guanabara Bay and compare these data to values determined through spectral optics using the Sentinel-3B Satellite OLCI (Ocean Land Color Instrument) during the winter and summer seasons of 2018, 2019, 2020, 2021, and 2022. This is done to highlight the impacts anthropogenic environmental hazards have on the marine ecosystem and human beings. Ten aquatic sediment field collection points were selected by triangulated irregular network (TIN). Samples were subjected to analysis by X-ray diffraction (XRD), scanning electron microscopy (SEM), electron dispersion spectroscopy (EDS), and transmission electron microscopy (TEM), which enabled a detailed analysis using scanning transmission electron microscopy (STEM). Geospatial analyses using Sentinel-3B OLCI Satellite images considered Water Full Resolution (WFR) at 300m resolution, in neural network (NN), normalized at 0.83µg/mg. A maximum average spectral error of 6.62% was utilized for the identification of the levels of Absorption Coefficient of Detritus and Gelbstoff (ADG443_NN) at 443m-1, Chlorophyll-a (CHL_NN) (m-3), and Total Suspended Matter (TSM_NN) (g m-3) at 581 sample points. The results showed high levels of ADG443_NN, with average values as high as of 4444m-1 (summer 2021). When related to the analyses of nanoparticulate sediments and ultrafine particles collected in the field, they showed the presence of major chemical elements such as Ge, As, Cr, and others, highly toxic to human health and the aquatic environment. The application of satellite and terrestrial surveys proved to be efficient, in addition to the possibility of this study being applied to other hydrological systems on a global scale.

Proximity Queries on Point Clouds using Rapid Construction Path Oracle

The prevalence of computer graphics technology boosts the developments of point clouds in recent years, which offer advantages over terrain surfaces (represented by Triangular Irregular Networks, i.e., TINs) in proximity queries, including the shortest path query, the k-Nearest Neighbor (kNN) query and the range query. Since (1) all existing on-the-fly and oracle-based shortest path query algorithms on a TIN are very expensive, (2) all existing on-the-fly shortest path query algorithms on a point cloud are still not efficient, and (3) there are no oracle-based shortest path query algorithms on a point cloud, we propose an efficient (1+ε)-approximate shortest path oracle that answers the shortest path query for a set of Points-Of-Interests (POIs) on the point cloud, which has a good performance (in terms of the oracle construction time, oracle size and shortest path query time) due to the concise information about the pairwise shortest paths between any pair of POIs stored in the oracle. Our oracle can be easily adapted to answering the shortest path query for any points on the point cloud if POIs are not given as input, and also achieve a good performance. Then, we propose efficient algorithms for answering the (1+ε)-approximate kNN and range query with the assistance of our oracle. Our experimental results show that when POIs are given (resp. not given) as input, our oracle is up to 390 times, 30 times and 6 times (resp. 500 times, 140 times and 50 times) better than the best-known oracle on a TIN in terms of the oracle construction time, oracle size and shortest path query time, respectively. Our algorithms for the other two proximity queries are both up to 100 times faster than the best-known algorithms.

A Single Data Extraction Algorithm for Oblique Photographic Data Based on the U-Net

In the automated modeling generated by oblique photography, various terrains cannot be physically distinguished individually within the triangulated irregular network (TIN). To utilize the data representing individual features, such as a single building, a process of building monomer construction is required to identify and extract these distinct parts. This approach aids subsequent analyses by focusing on specific entities, mitigating interference from complex scenes. A deep convolutional neural network is constructed, combining U-Net and ResNeXt architectures. The network takes as input both digital orthophoto map (DOM) and oblique photography data, effectively extracting the polygonal footprints of buildings. Extraction accuracy among different algorithms is compared, with results indicating that the ResNeXt-based network achieves the highest intersection over union (IOU) for building segmentation, reaching 0.8255. The proposed “dynamic virtual monomer” technique binds the extracted vector footprints dynamically to the original oblique photography surface through rendering. This enables the selective representation and querying of individual buildings. Empirical evidence demonstrates the effectiveness of this technique in interactive queries and spatial analysis. The high level of automation and excellent accuracy of this method can further advance the application of oblique photography data in 3D urban modeling and geographic information system (GIS) analysis.

EXPERIMENTING TIN DATA STRUCTURE FOR REPRESENTING PLANE SURFACE AND SUBSURFACE SPATIAL OBJECTS - PRELIMINARY WORK

Abstract. Understanding and analyzing complex spatial objects involves the integration of surface, terrain, and subsurface data into 3D spatial models. However, describing and efficiently analyzing the connections between these data model is particularly challenging. This study aims to generate TIN data structure that enable the 3D representation of surface terrain subsurface integration. Furthermore, to ensure accurate representation within a unified 3D model, spatial relationships between the TIN data model could be obtained. Consequently, three – dimensional Triangular Irregular Networks (3D TIN) mesh of spatial objects representations and indexing structures were created. The integration of spatial objects, encompassing surface, terrain, and subsurface elements, is realized through a unified model. This synergy is achieved by combining the distinct 2D and 3D topology structures of each component. The resulting comprehensive model captures the intricate relationships and interactions within the spatial environment. This study provides additional insight into the representation and analysis of surface, terrain, and subsurface management in 3D spatial models. Hence, better decision making, resource management, underground utility installations, and evaluation of spatial objects will be made possible in our future work.

GeoPDNN 1.0: a semi-supervised deep learning neural network using pseudo-labels for three-dimensional shallow strata modelling and uncertainty analysis in urban areas from borehole data

Abstract. Borehole data are essential for conducting precise urban geological surveys and large-scale geological investigations. Traditionally, explicit modelling and implicit modelling have been the primary methods for visualizing borehole data and constructing 3D geological models. However, explicit modelling requires substantial manual labour, while implicit modelling faces problems related to uncertainty analysis. Recently, machine learning approaches have emerged as effective solutions for addressing these issues in 3D geological modelling. Nevertheless, the use of machine learning methods for constructing 3D geological models is often limited by insufficient training data. In this paper, we propose the semi-supervised deep learning using pseudo-labels (SDLP) algorithm to overcome the issue of insufficient training data. Specifically, we construct the pseudo-labels in the training dataset using the triangular irregular network (TIN) method. A 3D geological model is constructed using borehole data obtained from a real building engineering project in Shenyang, Liaoning Province, NE China. Then, we compare the results of the 3D geological model constructed based on SDLP with those constructed by a support vector machine (SVM) method and an implicit Hermite radial basis function (HRBF) modelling method. Compared to the 3D geological models constructed using the HRBF algorithm and the SVM algorithm, the 3D geological model constructed based on the SDLP algorithm better conforms to the sedimentation patterns of the region. The findings demonstrate that our proposed method effectively resolves the issues of insufficient training data when using machine learning methods and the inability to perform uncertainty analysis when using the implicit method. In conclusion, the semi-supervised deep learning method with pseudo-labelling proposed in this paper provides a solution for 3D geological modelling in engineering project areas with borehole data.

ANALYSIS OF MEASURING STOCK OPNAME OF CEMENT SUPPORTING RAW MATERIALS BY TOTAL STATION

PT Semen Indonesia (Persero) Tbk is a BUMN (State-Owned Enterprise) company that operates in the cement industry in Indonesia and even abroad. PT Semen Indonesia (Persero) Tbk is located in Sumberarum Village, Kerek District, Tuban Regency, East Java Province. PT. Semen Indonesia (Persero) Tbk implements an open pit mining system using the Quarry method. This company is always close to mining activities where they can run if they meet the requirements of the Work Plan and Budget (RKAB) requirements per the Decree of the Minister of Energy and Mineral Resources No. 1806 K/30/MEM/2018. Mining activity planning is the most crucial aspect in designing mine designs, as a reference for achieving production targets and analyzing stock reserves of production raw materials. Stock taking is a temporary storage place after undergoing the transportation process from the mining front so that it can be ensured that the quality of the limestone can be maintained as originally. (Aliyusra Jolo, 2017). The method used in this research involves field surveys, taking detailed points according to the shape of the appearance, and processing the data using AutoCAD Civil 3D software which has a Triangular Irregular Network (TIN) working concept. The results of the research show that in stock-taking, after calculating the area and volume, we obtained an area of 4634,266 m² for stock opname 1, an area of 2916,728 m² for stock-taking 2, 1669,189 m² for stock-taking 3, and the total area of stock opname was 6298,750 m². And for the volume itself, the resulting volume was 24,204,084 m³ for stock opname 1, an area of 14,263,732 m³ for stock opname 2, 4321,165 m³ for stock opname 3, and the overall stock opname area was 42,788,981m³. As a result, this limestone stock will be used as a supporting material for cement production.

Meta-Universe Enabled Digital Modeling for Urban 3D Reality Visualization

Abstract The rapid acquisition of 3D data with high accuracy and efficiency, along with the reduction of production cycles and costs, are pressing challenges in the field of 3D reality modeling. This paper introduces a novel approach for urban 3D real-view modeling from a metacosmic perspective. Utilizing tilt-shift photography technology, this method captures three-dimensional data of urban scenes under the meta-universe framework. The data are then processed through an integration principle, combining DOM (Digital Orthophoto Map), DEM (Digital Elevation Model), and vector data to filter out interference. Subsequently, a high-density digital point cloud is generated using tilted imagery combined with aerial triangulation. This point cloud, along with a TIN (Triangulated Irregular Network) model, facilitates the construction of a comprehensive three-dimensional visualization model of urban environments, enabling detailed digital analysis. Data presentation demonstrates that with an increase in the number of image control points, the planimetric error decreases from 0.0536 m to 0.0388 m, reflecting a 27.61% improvement. Similarly, elevation accuracy improves from 0.0927 m to 0.0539 m, marking a 41.86% enhancement. This methodology supports the creation of highly precise and cost-effective three-dimensional realistic models of urban built-up areas, providing robust data support for the development and management of smart cities.

Multifiltering Algorithm for Enhancing the Accuracy of Individual Tree Parameter Extraction at Eucalyptus Plantations Using LiDAR Data

Accurately quantifying individual tree parameters is a critical step for assessing carbon sequestration in forest ecosystems. However, it is challenging to gather comprehensive tree point cloud data when using either unmanned aerial vehicle light detection and ranging (UAV-LiDAR) or terrestrial laser scanning (TLS) alone. Moreover, there is still limited research on the effect of point cloud filtering algorithms on the extraction of individual tree parameters from multiplatform LiDAR data. Here, we employed a multifiltering algorithm to increase the accuracy of individual tree parameter (tree height and diameter at breast height (DBH)) extraction with the fusion of TLS and UAV-LiDAR (TLS-UAV-LiDAR) data. The results showed that compared to a single filtering algorithm (improved progressive triangulated irregular network densification, IPTD, or a cloth simulation filter, CSF), the multifiltering algorithm (IPTD + CSF) improves the accuracy of tree height extraction with TLS, UAV-LiDAR, and TLS-UAV-LiDAR data (with R2 improvements from 1% to 7%). IPTD + CSF also enhances the accuracy of DBH extraction with TLS and TLS-UAV-LiDAR. In comparison to single-platform LiDAR (TLS or UAV-LiDAR), TLS-UAV-LiDAR can compensate for the missing crown and stem information, enabling a more detailed depiction of the tree structure. The highest accuracy of individual tree parameter extraction was achieved using the multifiltering algorithm combined with TLS-UAV-LiDAR data. The multifiltering algorithm can facilitate the application of multiplatform LiDAR data and offers an accurate way to quantify individual tree parameters.

Fast UAV Image Mosaicking by a Triangulated Irregular Network of Bucketed Tiepoints

To take full advantage of rapidly deployable unmanned aerial vehicles (UAVs), it is essential to effectively compose many UAV images into one observation image over a region of interest. In this paper, we propose fast image mosaicking using a triangulated irregular network (TIN) constructed from tiepoints. We conduct pairwise tiepoint extraction and rigorous bundle adjustment to generate rigorous tiepoints. We apply a bucketing algorithm to the tiepoints and generate evenly distributed tiepoints. We then construct a TIN from the bucketed tiepoints and extract seamlines for image stitching based on the TIN. Image mosaicking is completed by mapping UAV images along the seamlines onto a reference plane. The experimental results showed that the image mosaicking based on a TIN of bucketed tiepoints could produce image mosaics with stable and fast performance. We expect that our method could be used for rapid image mosaicking.

SEAMLINE OPTIMIZATION FOR UAV IMAGE MOSAICKING USING GEOMETRY OF TRIANGULATED IRREGULAR NETWORK

Abstract. For efficient UAV (Unmanned Aerial Vehicle) image monitoring, it is essential to mosaic multiple UAV images into one seamless image. In a mosaicked image, relief displacement of terrain is a major source of error. It is difficult to form seamlines that avoid all areas of relief displacements. A seamline determination method alone is limited in reducing the mismatch error on mosaicked image. In this study, we constructed a TIN (Triangulated Irregular Network) using tiepoints generated by rigorous bundle adjustments. We detected the regions where relief displacement occurred using the slope of TIN facets. It was found that the error of mosaicked image were mostly on TIN facets with high slopes. Our method generated a mosaicked image after elimination of error-prone region and showed that the distortions were effectively removed. This study showed that the proposed method could produce mosaicked images with stable quality using geometric clues of TIN. We expected that our method can be used for UAV image mosaicking robust to mismatching factors.

3D Kriging interpolation for traffic noise visualization: designing noise observation points and valuation of spatial interpolation accuracy

Identifying the risk of traffic noise is vital in minimizing traffic noise pollution in urban areas. As noise travels in every direction, 3D visualization of traffic noise is essential, which involves visualising traffic noise along the facades of buildings. A standard traffic noise model is necessary to calculate traffic noise levels, as several factors affect traffic noise. Moreover, designing noise observation points in 3D and spatial interpolation play significant roles in 3D noise visualisation. Therefore, this study demonstrates the results by elaborating on the spatial interpolation and designing noise observation points. A noise observation point consists of four parameters in 3D space. Generally, Inverse Distance Weighted (IDW), Triangular Irregular Network (TIN), and Kriging do not support the interpolation of four parameters in 3D. However, 3D Kriging in Empirical Bayesian Kriging provides significant opportunities to interpolate noise levels in 3D. However, the elements of the function of spatial interpolations are vital for accuracy. The 3D Kriging uses different variograms according to semivariance. This variogram directly impacts the weighting factor of 3D Kriging. Therefore, this study develops a comparison to identify the impact of different variograms on the accuracy of 3D Kriging interpolation on traffic noise.

Comparison and analysis of ground seed detectors and interpolation methods in airborne LiDAR filtering

Ground seed detectors and interpolation methods are fundamental components of filtering algorithms. However, the performance of different detectors and interpolation methods typically varies, and few studies have been conducted on the adaptability of different detectors and interpolation methods to different terrains. Therefore, we compare three ground seed detectors (cylindrical neighborhood (CN), fixed grid (FG), and moving window (MW)) and three interpolation methods (triangulated irregular network (TIN), thin plate spline (TPS), and inverse distance weighting (IDW)). In addition, nine filters are constructed by combining the three ground seed detectors and the three interpolation methods to evaluate their comprehensive influences. To assess the performance of these detectors, interpolation methods, and filters, fifteen ISPRS-supplied light detection and ranging (LiDAR) benchmark datasets are utilized in the experiment. The findings indicate that the CN-TPS filter, which combines the CN detector and TPS method, achieves superior performance across mean Pg (99.16 % – correctly classified ground points divided by all extracted ground points), RMSE (0.44 m – root mean squared error), and total error (3.78 %). Moreover, the filtering methods are mainly affected by the performance of the ground seed detector and are less affected by the selected interpolation method. These results can be used to provide a valuable reference for designing an optimal LiDAR filtering algorithm for varied terrain types and applications.

Machine learning insights of anthropogenic and natural influences on riverbed deformation in a large lowland river

As escalating environmental pressures threaten the world's river systems, understanding the driving factors influencing their geomorphological changes is of critical global importance. This study illuminates the complex interaction between natural and anthropogenic factors that drove geomorphological changes along the upper Apalachicola River (UAR) from 1960 to 2010. This study utilized LiDAR point cloud data and hydrographic surveys conducted between 1960 and 2010 to identify the primary factors driving riverbed changes. A triangular irregular network (TIN) was used to convert the combined datasets from each survey, following coordinate transformation, into a 1.5 m-resolution digital elevation model (DEM) raster. The DEM of difference (DEMoD) was computed by subtracting the 1960 DEM from that of 2010, and the total sediment loss and gain were then determined by multiplying the DEMoD by the DEM resolution. A random forest (RF) regression model was used to determine the most influential factors contributing to riverbed deformation. The results revealed substantial differences in sediment loss and gain across the river. The UAR, divided into three segments, experienced sediment loss of about −6.56 × 106 m3 between 1960 and 2010, with the farthest upstream section (RM 106 to 98) losing −3.14 × 106 m3, which is ~48 % of the sediment losses in the study area. Some sections of the channel have deepened significantly, with a pronounced depth difference of −17.20 m between RM 92 and 94, potentially caused by the presence of limestone and the enlargement of a sinkhole or spring on the riverbed. The RF model identified the dam (>28 %) as the most crucial anthropogenic factor in riverbed deformation, followed by the presence of dikes (~17—18 %), then historical dredging (~7—8 %), highlighting the substantial impact of artificial structures on riverbed deformation. In contrast, rock removal, while potentially impactful in certain locations, had negligible effects (>0.5 %) at the system scale. This study highlights the interaction of natural and anthropogenic factors in river geomorphology and the potential for machine learning to identify these dynamics and inform data-driven river management and conservation strategies.

Three-dimensional visualisation of traffic noise based on the Henk de-Klujijver model

Abstract Visualisation of road traffic noise is vital for traffic noise planning policies. Several factors affect the noise from road traffic with physical and environmental conditions. Collecting noise levels around the world is not a possible task. Therefore, calculating noise levels by a valid noise model, and spatial interpolations, is prime to traffic noise visualisation. In this study, the Henk de Klujijver noise model is used. Designing noise observation points (Nops) embedding with a three-dimensional (3D) building model and identifying the best suitable spatial interpolation are important to visualise the traffic noise accurately. However, interpolating noise in 3D space (vertical direction) is a more complex process than interpolating in two-dimensional (2D) space. Flat triangles should be eliminated in the vertical direction. Therefore, the structure of Nop has a major influence on spatial interpolation. Triangular Irregular Network (TIN) interpolation is more accurate for visualising traffic noise as 3D noise contours than Inverse Distance Weighted and kriging. Although kriging is vital to visualise noise as raster formats in 2D space. The 3D kriging in Empirical Bayesian shows a 3D voxel visualisation with higher accuracy than 3D TIN noise contours.