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Developing an effective emergency collaborative distribution system is critical to improve on-time response performance, especially considering secondary disasters. To address the challenge, this paper investigates a bi-objective robust optimisation model on relief collaborative distribution among three echelons of authorities, the province, the municipality, and the county, which aims to minimise the total travel time and the total humanitarian logistics cost simultaneously. The optimal location of relief supply facilities and the relief distribution schemes will be determined by the optimisation model, which considers uncertain demand and travel time. Moreover, two robust optimisation methods are utilised to deduce the robust counterparts of the proposed model. An epsilon-constraint-based approach is used to solve the bi-objective optimisation model. A real-world case study based on an earthquake and aftershocks with different magnitudes in Yunnan Province is provided. The results show that incorporating secondary disaster scenarios contributes to reducing the total travel time and cost. For making full use of emergency resources and preventing situations from worsening, the centralised decision scheme is more effective than the decentralised one. The uncertainty of demand of primary disaster relief has a higher impact on the optimal solution than that of travel time. [ABSTRACT FROM AUTHOR]

Deep learning-based super-resolution methods have been successfully applied to digital elevation model (DEM) downscaling studies by designing structures and loss functions of the model. However, little attention has been paid to the design of super-resolution models that can maintain the hydrological characteristics of the DEM, which is important for hydrological studies. This study introduces a super-resolution model that integrates hydrologic knowledge (HKSRCGAN), with the aim to effectively maintain topographic features as well as the hydrologic connectivity of the DEM. The hydrological knowledge derived from surface flow direction and hydrological features are integrated into a deep learning algorithm to guide model training. The 30 m spatial resolution FABDEM is used to demonstrate the utility of the proposed method. Results show that the HKSRCGAN outperforms the bicubic interpolation, SRCNN, SRGAN, SRResNet and TfaSR methods in reducing topographic errors and maintaining hydrologic characteristics. In the test area, the entropy difference analysis shows that the DEM generated by HKSRCGAN is similar to the information contained in the reference DEM. Furthermore, super-resolution models integrating hydrological knowledge are valuable for modeling terrain primarily shaped by gravity and surface water flows. In the future, deep learning-based models integrating hydrologic knowledge are expected to be applied in DEM upscaling to maintain consistent hydrological characteristics. [ABSTRACT FROM AUTHOR]

Objective: Police officers often find themselves responding to calls that do not involve criminal behavior, but calls related to individuals experiencing emotional distress or mental illness that requires police response. This research aims to identify high-risk places of mental health calls for service (MHCFS) in an urban setting and compare place features of these locations by police response (locations where a CIT-trained officer is dispatched versus a non-trained officer dispatched). Data/Methods: Our research uses Atlantic City Police Department's calls for service data from 1 January 2019 through 30 December 2022, including the incidents of mental incompetence with location components. After removing the calls that were not coded as mental health, a total of 2229 incidents involving perceived mental illness were geocoded and compared. We used RTMDx software to generate risk terrain models for MHCFS in Atlantic City, NJ. Findings: The RTM analysis yielded no association between places of interest and MHCFS for calls involving non-CIT-trained officers. Yet, we identified five high-risk activity spaces of MHCFS where officers with CIT training were involved. Grocery stores (RRV = 2.873), places of leisure (RRV = 2.764), Health & Wellness Facilities (RRV = 2.624), Eateries & Restaurants (RRV = 2.547), and Convenience & Variety Stores (RRV = 2.280) are the risk factors identified by the RTM analysis. Given the characteristics of places identified by risk-terrain modeling, we reason that the characteristics of these places indicate that individuals experiencing perceived or actual mental health problems hang out in spaces with ease of access and in spaces where they can stay relatively longer with minimal attention. Conclusion: Our research suggests that there is an association between spatial factors and MHCFS that are responded to by CIT-trained officers. City officials should consider deploying additional resources and mental health staff to these places to address the issue of mental health in urban settings. Future research should consider testing whether the places we identified in our analysis are also high-risk places for MHCFS in other urban environments. [ABSTRACT FROM AUTHOR]

AbstractModels play a pivotal role in geography and geography education. However, there is a notable lack of didactic research on their application in the classroom. This qualitative study investigates the didactic integration of four established human geography phase models within upper secondary geography education, focusing on their potential to enhance modeling competence. Based on a framework of modeling competence, the study first illustrates the existing potential of the specific competence orientation for the use of models in teaching. The empirical analysis of 16 upper secondary school textbooks from various publishers and federal states in Germany however indicates that the didactic integration of the models examined is mostly deficient and makes only a small contribution to the potential promotion of learners’ modeling competence. [ABSTRACT FROM AUTHOR]

The determination of physical heights is of key importance for a wide spectrum of geoscientific applications and, in particular, for engineering projects. The main scope of the present work is focused on the determination of a high-accuracy and high-resolution gravimetric and hybrid geoid model, to determine orthometric heights without the need of conventional leveling. Both historical and newly acquired gravity data have been collected during dedicated gravity campaigns, around the location of a dedicated GNSS network as well as areas where the existing land gravity database presented voids. Geoid determination was based on the classical remove–compute–restore (RCR) technique and spectral and stochastic approaches. The low frequencies have been modeled based on the XGM2019e global geopotential model (GGM) and the topographic effects have been evaluated with the residual terrain model (RTM) reduction. The evaluation of the final geoid model was performed over 462 GNSS/leveling benchmarks (BMs), where the newly determined gravimetric geoid has shown an improvement of 3.1 cm, in the std of the differences to the GNSS/leveling BMs, compared to the latest national geoid model. A deterministic and stochastic fit to the GNSS/leveling data has been performed, investigating various choices for the parametric models and analytical covariance functions. The scope was to determine a hybrid geoid model, tailored to the area and GNSS/leveling data, which will be the one used for the direct estimation of high-accuracy orthometric heights from GNSS observations. After the deterministic fit, a std to the GNSS/leveling data of 10.1 cm has been achieved, with 54.8% and 83.1% of the absolute height differences being below the 1 cm and 2 cm per square root km of baseline length. The final hybrid geoid model, i.e., after the stochastic treatment of the adjusted residuals, gave a std of the difference to the GNSS/leveling data of 1.1 cm, with 99.8% and 99.9% of the height difference being smaller than the 1 cm and 2 cm standard errors, thus achieving a 1 cm accuracy regional geoid. [ABSTRACT FROM AUTHOR]

A precise gravimetric geoid model is computed utilising Stokes's formula, supposing an absence of topography above the geoid. Subsequently, the geoid model undergoes a simple correction for topographic masses, the constant density is taken as 2670 kg/m3. Notably, the true density of topographical mass deviates by approximately ±20% from this constant value. Recently, the University of New Brunswick in Canada released a global topographical density model at a 30 arc-second resolution. This paper investigates the impact of incorporating this model on the precision of the gravimetric geoid within a mountainous region in the Colorado test region. Numerical findings reveal that variations in geoid undulation attributable to this model can extend to a few decimetres, a discrepancy that cannot be neglected in geoid modelling with one-centimetre precision. It is therefore recommended that the considerable impact of topographic density fluctuations on geoid determination be taken into account, particularly in mountainous regions. [ABSTRACT FROM AUTHOR]

Coastal and riverine levees are prone to wave and steady-flow overtopping, which can result in breaching and consequential flooding of the hinterland. Full-scale field experiments on a levee are a sound approach to understanding of the underlying physical processes without suffering from scale effects, provided that accurate and valuable data can be collected. This paper outlines how close-range photogrammetry was applied to monitor a levee's morphological evolution continuously and intermittently during wave overtopping. High-resolution spatial topographical models were obtained from which erosion rates could be quantified. Two camera setups—a system of multiple stationary synchronized cameras and a single mobile camera—were used for photo acquisition. The first configuration enabled reconstruction of the target zone as a dynamic scene and reduced photogrammetric analysis time through simultaneous processing of several frames [four-dimensional (4D) processing]. The second configuration applied the structure-from-motion (SfM) technique using multiple overlapping photographs to obtain three-dimensional (3D) elevation models at specific moments. Both approaches resulted in high-resolution digital elevation models (DEMs) with low mean absolute errors (MAEs) on the order of a few centimeters. Furthermore, photogrammetry proved to be highly flexible and could be applied to different types of levees with various cover layers, hydraulic loads, and meteorological conditions. Practical Applications: Monitoring erosion due to wave overtopping of levees was achieved here using close-range photogrammetry in two series of field experiments with distinct camera setups. The experiments were conducted at 1∶1 scale at the Living Lab Hedwige Prosperpolder to study the erosion processes of the levee's clay's protection layer (1-m-thick clay) on the landward-side slope subjected to loads by overtopping waves. The experiments were conducted on a levee cover in (1) its natural state; and (2) after treating clay with lime for reinforcement. In the first series of experiments, a system of 10 synchronized stationary cameras was used to capture photos. To focus on the erodibility of the natural clay cover, the 0.2-m-thick grass cover was removed over an area of 14 m2 at three locations on the slope prior to testing. In the second series, a single camera was moved around the scene at specific time intervals. An object featuring a building was also embedded in each new slope revetment to evaluate the effects on erosion of flow around obstacles. Both generated accurate erosion maps, with a slightly higher accuracy and ease of post-treatment for the multiple-camera setup. [ABSTRACT FROM AUTHOR]

This article addresses a significant aspect of evaluating and comparing spatial forest data from various databases developed and maintained in Poland and globally. The study focused on the application of these data to create terrain passability maps, which are useful in planning military and crisis operations. The research was conducted in a test area near Warsaw, encompassing the Kampinos Forest. In the study, the "forest" layers from the tested databases were compared. Their spatial extents were analyzed, and terrain passability maps were generated in different configurations, which were comprehensively compared with one another. The results indicated that the quality and detail of forest data are not critical for generating passability maps. Only in the case of creating highly detailed maps does the use of precise data prove justified. As the level of detail in the maps decreases, they become increasingly similar, reducing the influence of the forest data on their accuracy and operational applicability. The study enabled the selection of the most accurate data sources on forested areas—those that most faithfully represent the structure of forested regions in Poland. [ABSTRACT FROM AUTHOR]

In ground mobile robots, effective path planning relies on their ability to assess the types and conditions of the surrounding terrains. Neural network‐based methods, which primarily use visual images for terrain classification, are commonly employed for this purpose. However, the reliability of these models can vary due to inherent discrepancies between the training images and the actual environment, leading to erroneous classifications and operational failures. Retraining models with additional images from the actual operating environment may enhance performance, but obtaining these images is often impractical or impossible. Moreover, retraining requires substantial offline processing, which cannot be performed online by the robot within an embedded processor. To address this issue, this paper proposes a neural network‐based terrain classification model, trained using an existing data set, with a novel uncertainty rejection filter (URF) for terrain‐aware path planning of mobile robots operating in unknown environments. A robot, equipped with a pretrained model, initially collects a small number of images (10 in this work) from its current environment to set the target uncertainty ratio of the URF. The URF then dynamically adjusts its sensitivity parameters to identify uncertain regions and assign associated traversal costs. This process occurs entirely online, without the need for offline procedures. The presented method was evaluated through simulations and physical experiments, comparing the point‐to‐point trajectories of a mobile robot equipped with (1) the neural network‐based terrain classification model combined with the presented adaptive URF, (2) the classification model without the URF, and (3) the classification model combined with a nonadaptive version of the URF. Path planning performance measured the Hausdorff distances between the desired and actual trajectories and revealed that the adaptive URF significantly improved performance in both simulations and physical experiments (conducted 10 times for each setting). Statistical analysis via t‐tests confirmed the significance of these results. [ABSTRACT FROM AUTHOR]

In order to optimize the pressure relief gas extraction process for the 1504 working face in East 2 of Dalong Coal Mine based on its mining and gas conditions, a physical model of pressure relief gas extraction in the airspace using two preliminary extraction processes—a high-level oblique borehole and a directional long borehole—was established using COMSOL 6.2 software. The changes in the gas extraction effect of high-level oblique boreholes were analyzed through a simulation of the advancement of the working face, and the reasons for the low utilization rate of the high-level oblique boreholes were outlined. The effects of the horizontal distance of the directional long boreholes from the side of the air return lane, the borehole spacing, and the negative pressure of the boreholes on the gas extraction effect were analyzed, and the gas extraction process of the directional long boreholes was optimized and applied in the field. The results showed that the directional long borehole gas extraction process had a better extraction effect, a higher borehole utilization rate, and superior cost savings, and was thus was the preferred process. Additionally, the optimal parameters were a 30 m horizontal distance of the boreholes from the side of the air return lane, a 30 m spacing between the boreholes, and a 20 kPa negative extraction pressure. [ABSTRACT FROM AUTHOR]

We propose a lightweight method for constructing a global digital elevation and terrain database in consideration of the satellite flight path. First, a global maximum/minimum elevation database with a grid size of 0.05° × 0.05° was generated using Shuttle Radar Topography Mission data and the Global Multi-resolution Terrain Elevation Data along with other multi-source digital elevation model (DEM) data. A global database of maximum terrain fluctuation with a grid size of 0.05°× 0.05° and distances of 140 and 700 m was then constructed on the basis of the ICESat-2 satellite flight path and the main frame/super frame distance criterion. The databases, which achieve global coverage while occupying a small storage space, can be used to assist in the in-orbit preprocessing of onboard data. The database accuracy was evaluated using ICESat-2 data. In the 10 validation areas, the global DEM database had an error of only 3 m in two validation areas, and the error was less than the accuracy range of each DEM data source. The global terrain database can characterize the degree of surface relief in each verification area, confirming the effectiveness of the method used in this study. [ABSTRACT FROM AUTHOR]

The catastrophic flood of 1813 that occurred in the Western Carpathians region was the largest natural disaster ever recorded here. The 1812/1813 winter season was very cold with abundant snow cover. Spring and summer months were also rich in liquid precipitation, further increasing soil saturation and catchment runoff. Torrential rains were reported to occur from 24 to 26 August when two deep low-pressure systems met over the Western Carpathians above the headwaters of large rivers like Váh and Hron. This resulted in a flood with the flood wave being 10 m above the long-term river stage. In this study we summarize the findings from our archival research to report on the causes, severity and extent of the 1813 flood. Using three-dimensional terrain modelling we have reconstructed the culmination water level in two towns affected by the flood from the rivers Váh and Hron using historical maps and preserved flood marks. [ABSTRACT FROM AUTHOR]

Ground-based Interferometric Synthetic Aperture Radar (GB-InSAR) is a valuable technique for monitoring deformation of landslides. GB-InSAR can construct high-resolution 2D images in a range-doppler plane. However, it is difficult for geo-engineers who are unfamiliar with radar monitoring geometry to interpret the results. Geometric mapping method has been applied to the co-registration of terrain models and radar images for the deformation zonation, but the mismatching correction with GB-InSAR and terrain model is not fully investigated by existing researches. To address this issue, this paper proposes a method exploiting an optimum linear transformation based on ground control points (GCPs). The proposed method was evaluated on simulated data and a field campaign in an open pit mine. The deformation mapping result of the method was verified by a collapse event. The method proposed in this paper has an RMSE value ranging from 0.502 to 0.720 m (@700 m average monitoring range) when the average density of the terrain point cloud is 0.5 m. Although this method cannot accurately evaluate the antenna footprint vector deflection parameters, it can meet the needs of coarse matching calibration in relatively flat slope sub-target areas of open-pit mines for engineering applications. [ABSTRACT FROM AUTHOR]

Spatial autoregressive (SAR) models are often used to explicitly account for the spatial dependence underlying geographic phenomena. However, traditional SAR models are specified using a single SAR coefficient, assuming constant spatial dependence over space. This assumption oversimplifies the situation where the true spatial autoregressive process varies in strength; the consequences of ignoring heterogeneous autocorrelation remain to be discussed. This study proposes a heterogeneous spatial autocorrelation model by extending the spatial lag model (SLM). The new model includes change point detection for identifying patterns of spatially varying autocorrelation strengths, a SAR coefficient matrix for representing heterogeneous spatial autocorrelation, and maximum likelihood estimation for determining multiple SAR coefficients. Monte Carlo simulations demonstrate that the proposed method is effective in modeling SAR processes with heterogeneous autocorrelation patterns, while traditional SLM inflates uncertainties in the regression coefficients when a heterogeneous autocorrelation structure is not accounted for. We further applied the new method to an empirical analysis of traffic crashes in the Greater Perth Area, Australia. The heterogeneous spatial autocorrelation model reduces model RMSE by 42% (compared with traditional SLM). Results from both simulation and empirical studies indicate that spatially varying autocorrelation strengths should be considered for SAR processes and relevant applications. [ABSTRACT FROM AUTHOR]

As an effective means of coal burst prevention, large‐diameter drilling pressure relief technology have been widely used in coal burst mines in China. Engineering practice shows that drilling pressure relief has obvious time‐effect. Through the establishment of drilling pressure relief numerical model, the evolution law of stress field and plastic zone in different periods after drilling was studied. The evolution of the pressure relief effect of large‐diameter borehole is closely related to the development degree of cracks around the borehole. The stress transfer process after drilling can be divided into the pressure relief beginning stage, rapid pressure relief stage, and pressure stable stage. The pressure relief time effect is affected by borehole parameters. When the diameter increases from 100 to 250 mm, the time required for the stress transfer process is reduced by 50%. When the borehole spacing is reduced from 2 to 0.5 m, the time required for the stress transfer process is reduced by 33.3%. Changing the borehole depth has no significant effect on the stress transfer rate. Therefore, in the coal burst risk roadway, pre‐relief should be carried out to leave sufficient time for large‐diameter boreholes to make pressure relief work. [ABSTRACT FROM AUTHOR]

Path planning technology is of great consequence in the field of unmanned aerial vehicles (UAVs). In order to enhance the safety, path smoothness, and shortest path acquisition of UAVs undertaking tasks in complex urban multi-obstacle environments, this paper proposes an innovative composite improvement algorithm that integrates the advantages of the jellyfish search algorithm and the particle swarm algorithm. The algorithm effectively overcomes the shortcomings of a single algorithm, including parameter setting issues, slow convergence rates, and a tendency to become trapped in local optima. Additionally, it enhances the path smoothness, which improves the path optimisation. This enhances the capacity of UAVs to optimise their paths in environments characterised by multiple obstacles. To evaluate the practical effectiveness of the algorithm, a three-dimensional complex city model was constructed for the purposes of the study, and an adaptation function was designed for the purpose of evaluation. The experimental evaluation of 23 benchmark functions, the simulation test of the 3D city model, and 100 repetitive experiments demonstrate that the composite improved algorithm has a considerable advantage over the other comparative algorithms regarding performance. It exhibits fast convergence, high accuracy, and both global and local search capabilities, which enable the effective planning of a UAV flight path and the maintenance of good stability. In comparison to traditional algorithms, the composite improved algorithm demonstrably reduces the flight time and the number of obstacle avoidance manoeuvres required by the UAV. It provides robust technical support for the path planning of the UAV in complex urban environments and facilitates the advancement and implementation of related technologies. [ABSTRACT FROM AUTHOR]

The accurate estimation of Digital Elevation Models (DEMs) derived from satellite data is critical for numerous environmental applications. This study evaluates the accuracy and reliability of two satellite-derived elevation models, the ALOS World 3D and SRTM DEMs, specifically for their application in hydrological modeling. A comparative analysis with Terrestrial Laser Scanning (TLS) measurements assessed the agreement between these datasets. Multiple linear regression models were utilized to evaluate the relationships between the datasets and provide detailed insights into their accuracy and biases. The results indicate significant correlations between satellite DEMs and TLS measurements, with adjusted R-square values of 0.8478 for ALOS and 0.955 for the SRTM. To quantify the average difference, root mean square error (RMSE) values were calculated as 10.43 m for ALOS and 5.65 m for the SRTM. Additionally, slope and aspect analyses were performed to highlight terrain characteristics across the DEMs. Slope analysis showed a statistically significant negative correlation between SRTM and TLS slopes (R2 = 0.16, p < 4.47 × 10−10 indicating a weak relationship, while no significant correlation was observed between ALOS and TLS slopes. Aspect analysis showed significant positive correlations for both ALOS and the SRTM with TLS aspect, capturing 30.21% of the variance. These findings demonstrate the accuracy of satellite-derived elevation models in representing terrain features relative to high-resolution terrestrial data. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to understand the socioeconomic impact of flood events on households, especially household welfare in terms of changes in consumption and coping strategies to deal with flood risk. This study is based on Bihar, one of the most frequently flood-affected, most populous and economically backward states in India. Design/methodology/approach: Primary data were collected from 700 households in the seven most frequently flood-affected districts in Bihar. A total of 100 individuals from each district were randomly selected from flood-affected villages. Based on a detailed literature review, an econometric (probit) model was developed to test the null hypothesis of the availability of consumption insurance, and the multivariate probability approach was used to analyze the various coping strategies of these households. Findings: The results of this study suggest that flood-affected households maintain their consumption by overcoming various losses, including income, house damage and livestock loss. Households depend on financial transfers, borrowings and relief, and migrate to overcome losses. Borrowing could be an extra burden as the government compensates for house damage and crop loss late to the affected households. Again, there is no compensation to overcome livelihood loss and deal with occurrences of post-flood diseases, which further emphasizes the policy implications of strengthening the health infrastructure in the state and generating alternative livelihood opportunities. Originality/value: This study discusses flood risk in terms of changes in household welfare, identifies the most effective risk-coping capabilities of rural communities and contributes to the shortcomings of the government insurance and relief model. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-07-2023-0569 [ABSTRACT FROM AUTHOR]

Noise simulations are an important part of noise studies that investigate the impact of noise sources on the environment. In noise simulation, noise levels at receiver points are calculated based on the noise propagation paths between the receiver and source points. These paths are derived from the height of the terrain. In current calculation approaches implemented in noise simulation software, 3D polylines are used as input to describe the height of the terrain. These 3D polylines are semi-automatically generated to meet the highly demanding computing performance of simulation software. In addition, previous research showed that the reconstruction of appropriate height lines as used in noise simulation is very difficult to automate, if not impossible As a solution, this research investigates how noise propagation paths between receiver and source points can directly be generated from a Triangulate Irregular Network (TIN) without creating the height lines. This would allow us to use the automatically generated TIN as input for noise simulation instead of the height lines. In addition, a TIN enables better control of the quality of the data than height lines do. This study uses the 3D noise modeling guidelines of Common Noise Assessment Methods in Europe (CNOSSOS-EU). Algorithms have been developed and implemented in a prototype to generate and validate the paths between receiver and source points using a TIN that includes the buildings as well as the noise absorption properties of the terrain. The prototype is successfully tested on two scenarios from the Netherlands. Since CNOSSOS-EU guidelines were used, the prototype is applicable to the entire European Union and can be the first step in improving the automation of 3D noise modeling using currently available techniques and data. [ABSTRACT FROM AUTHOR]

This article presents the results of long-term monitoring of land subsidence in the Bielszowice area (Upper Silesian Coal Basin) using archival maps from the late 19th and 20th centuries, as well as contemporary LIDAR models from 2012 and 2022. The research work conducted included an analysis of subsidence caused by mining activities based on four terrain models: a historical terrain model obtained by digitizing Messtischblätter topographic maps, showing the land surface in 1883, a terrain model obtained by vectorizing Polish topographic maps from 1993, and LIDAR digital terrain models from 2012 and 2022. The study shows that over a period of 139 years, the study area subsided by an average of 9.5 m, which translated into an anthropogenic land subsidence rate of 68 mm/year and a subsidence volume of 100.5 million m3. The greatest subsidence occurred in the northern part of the study area, where basins with depths exceeding 30 m (the maximum subsidence amounted to 36 m) emerged. During the 139 years studied, land subsidence affected the entire area that was built up until 2022. Overall, 38.9% of built-up areas subsided by less than 10 m, 54.0% was subject to subsidence ranging between 10 and 20 m, and subsidence of more than 20 m affected 7.1% of the areas. Such large-scale subsidence in an urbanized area resulted in mining damage to houses and other infrastructure (e.g., railroads, roads); in extreme cases, some structures had to be demolished. Bielszowice is a good example of an area where spatial conflicts have emerged that have been related to the activities of industrial plants on the one hand and the development of urban areas on the other. [ABSTRACT FROM AUTHOR]

This study conducts an evaluation of the performance of seven ultra-high-degree Global Geopotential Models (GGMs) across Egypt, utilizing GNSS/leveling data as a basis for assessment. The models under investigation include SGG-UGM-2, XGM2019e_2159, SGG-UGM-1, GECO, EIGEN-6C4, EGM2008, and GGMplus. The evaluation procedure comprises three principal steps: an absolute assessment, the implementation of Residual Terrain Modelling (RTM), and the fitting of GGMs to GNSS/leveling data. Among the models assessed, GECO exhibited the highest performance in the absolute assessment, achieving a standard deviation (STD) of 0.310 m, while SGG-UGM-1 recorded the largest STD at 0.353 m. Given Egypt’s predominantly flat topography, the application of RTM yielded only modest benefits. Nonetheless, all models demonstrated significant enhancements in accuracy through the fitting process, with EIGEN-6C4 emerging as the most successful model, attaining an STD of 0.116 m in external assessment. The accuracy improvements following the fitting procedure ranged from 49 % to 63 % across all models assessed. [ABSTRACT FROM AUTHOR]

The level of detail (LOD) modelling of vector and terrain data is individual, resulting in geometric and topological inconsistencies in simplification processes. The three dimension Douglas Peucker (3D_DP)algorithm can realize gradual discrete point selection through threshold, which is mainly used in DEM synthesis, and its simplified process is very suitable for the dynamic establishment of massive data sets. A new LOD modeling method based on 3D_DP algorithm is proposed to simplify the consistency of river network vector elements and DEM in this paper. The specific steps are as follows: Firstly, the "Bending Adjustment Index (BAI)" is introduced to improve the 3D_DP algorithm, called the improved 3D_DP algorithm; Secondly, the DEM data is extracted into a 3D discrete point dataset, and the river line vector data is also converted into a discrete point dataset, assigned with elevation attributes, and merges with the DEM's 3D discrete points. The merged point datasets are equenced based on the importance of each point, which are computed by the improved 3D_DP algorithm. The order of deleting points is determined by the sequence and the corresponding model spatial errors are calculated with the deletion of points. Then, the DEM's 3D discrete points are constructed a Delaunay network with the river line as a mandatory constraint condition. The required triangulation is called in real time with the change of sight distance depending on the simple correspondence between screen projection error and model space error, and the unified LOD model for river line vector and DEM is established. The results show that the river's overall shape and the terrain's main features can be reserved under the same simplified factor based on the improved 3D_DP algorithm. The unified LOD model for the river network and DEM is feasible under the importance sequence of merged point datasets by the improved 3D_DP algorithm. Under the proper operation of data blocking, the rendering frame rate can meet practical application requirements. [ABSTRACT FROM AUTHOR]

Preprocessing Synthetic Aperture Radar (SAR) data is a crucial initial stage in leveraging SAR data for remote sensing applications. Terrain correction, both radiometric and geometric, and the detection of layover/shadow areas hold significant importance when SAR data are collected over mountainous regions. This study aims at investigating the impact of the Digital Elevation Model (DEM) used for terrain correction (radiometric and geometric) and for mapping layover/shadow areas on windthrow detection using COSMO SkyMed SAR images. The terrain correction was done using a radiometric and geometric terrain correction algorithm. Specifically, we evaluated five different DEMs: (i–ii) a digital terrain model and a digital surface model derived from airborne LiDAR flights; (iii) the ALOS Global Digital Surface Model; (iv) the Copernicus global DEM; and (v) the Shuttle Radar Topography Mission (SRTM) DEM. All five DEMs were resampled at 2 m and 30 m pixel spacing, obtaining a total of 10 DEMs. The terrain-corrected COSMO SkyMed SAR images were employed for windthrow detection in a forested area in the north of Italy. The findings revealed significant variations in windthrow detection across the ten corrections. The detailed LiDAR-derived terrain model (i.e., DTM at 2 m pixel spacing) emerged as the optimal choice for both pixel spacings considered. [ABSTRACT FROM AUTHOR]

The integration of ground-penetrating radar (GPR) with unmanned aerial vehicles (UAVs) enables efficient non-contact detection, performing exceptionally well in complex terrains and extreme environments. However, challenges in data processing and interpretation remain significant obstacles to fully utilizing this technology. To mitigate the effects of numerical dispersion, this paper develops a high-order finite-difference time-domain (FDTD(2,4)) three-dimensional code suitable for airborne GPR numerical simulations. The simulation results are compared with traditional FDTD methods, validating the accuracy of the proposed approach. Additionally, a Kirchhoff migration algorithm that considers the influence of the air layer is developed for airborne GPR. Different processing strategies are applied to flat and undulating terrain models, significantly improving the identification of shallowly buried targets. Particularly under undulating terrain conditions, the energy ratio method is introduced, effectively suppressing the interference of surface reflections caused by terrain variations. This innovative approach offers a new technical pathway for efficient GPR data processing in complex terrains. The study provides new insights and methods for the practical application of airborne GPR. [ABSTRACT FROM AUTHOR]

The topography of mountainous areas is characterized by large undulations, which lead to a very complex wind field at bridge sites in mountain valleys. The influence of oncoming wind speed on long-span bridges built in mountain valleys is quite pronounced. To investigate the wind characteristics at a bridge site in a mountain valley under different oncoming wind speeds, a wind tunnel test of a terrain model with a scaling ratio of 1:1000, where a long-span bridge would be built in the V-shaped canyon, was conducted. Uniform and atmospheric boundary layer (ABL) inflows were both applied, and the effect of different oncoming wind speeds (basic wind speeds of 6 m/s, 8 m/s, 10 m/s, 12 m/s, and 14 m/s) under three wind directions (0°, 30°, and 180°) on the wind characteristics at the main beam and two bridge towers were studied. The results indicate that increasing oncoming wind speed leads to decreased wind profiles and wind speed amplification factors and increased wind attack angles, while wind yaw angles remain largely unchanged. In addition, compared to ABL inflow, the variation of fluctuating wind characteristics is more pronounced with the oncoming wind speed under uniform inflow. Under uniform inflow conditions, increasing the oncoming wind speed causes decreased turbulence intensity, reduces the peak frequency of the power spectrum, and slows down the high-frequency decay rate. Under ABL inflow conditions, turbulence intensity and the power spectrum remain unchanged with different oncoming wind speeds. Additionally, the turbulent integral scale derived from fitting with the von Kármán wind spectrum is sufficiently accurate, and the variation in the turbulent integral scale is greatly influenced by the terrain. Furthermore, higher wind speeds result in stronger coherence between two points. When two points are at different locations but with the same spacing, the coherence function remains roughly the same. Locations with higher kurtosis and skewness values exhibit steeper probability density functions, with larger kurtosis and skewness coefficients typically found on the leeward side. High wind speeds are more detrimental to bridge safety, and appropriate preventive measures should be implemented in advance to address extreme conditions that may arise at high wind speeds. [ABSTRACT FROM AUTHOR]

The use of data and statistics along with computational systems heralded the beginning of a quantitative revolution in Geography. Use of simulation models (Cellular Automata and Agent‐Based Models) followed in the late 1990s, with ontology and epistemology of complexity theory and modelling being defined a little less than two decades ago. We are, however, entering a new era where sensors regularly collect and update large amounts of spatio‐temporal data. We define this 'Big Data' as geolocated data collected in sufficiently high volume (exceeding storage capacities of the largest personal hard drives currently available), that is updated at least daily, from a variety of sources in different formats, often without recourse to verification of its accuracy. We then identify the exponential growth in the use of complexity simulation models in the past two decades via an extensive literature review (broken down by application area), but also notice a recent slowdown. Further, a gap in the utilisation of Big Data by modellers to calibrate and validate their models is noted, which we attribute to data availability issues. We contend that Big Data can significantly boost simulation modelling, if certain constraints and issues are managed properly. [ABSTRACT FROM AUTHOR]

The objective of this article is to present a novel GIS plugin for classifying and georeferencing high‐resolution oblique imagery with the intention of creating landcover datasets for spatial analysis. The Mountain Image Analysis Suite (MIAS) is a newly released plugin for the open‐source software, QGIS. MIAS was developed with images from Mountain Legacy Project, the world's largest systematic collection of high‐resolution mountain images. It works with both grayscale and color imagery, including historical images that predate aerial and satellite imagery. MIAS encompasses four tools for classifying and georeferencing oblique images. The plugin accesses pretrained deep learning models from a PyTorch‐based segmentation network to automate the classification of landcover in oblique images. Monoplotting is accomplished through the construction of a virtual photograph simulating the view from the camera using a shaded relief model. Once the virtual photograph is produced, the user aligns the classified image to the virtual photograph using a set of control points. This allows the creation of a classified and georeferenced raster representing the landcover for the area visible in the original oblique image. Similar workflows to the one contained in MIAS have been used for landcover mapping with oblique images to a high level of accuracy. However, MIAS is the first piece of software to bring all stages of image analysis into a single platform. MIAS has many applications across diverse fields such as mountain research, ecological restoration, community‐based mapping, environmental planning, and more. [ABSTRACT FROM AUTHOR]

The current research focus on visualizing terrain features emphasizes quantification and detailed simulation, without adequately considering the impact of spatial–temporal variations in the terrain on human cognition. However, advancements in visualization technology, such as efficient and rapid construction of large‐scale three‐dimensional (3D) terrain scenes, real‐time dynamic display, and free‐roaming from any viewpoint, currently provide ample technical support for visualizing spatial–temporal information. Therefore, this article proposes a 3D terrain viewing model that considers the spatial–temporal changes in light intensity and incident direction in a terrain scene, based on the principles of radiometry and computer graphics theory and supported by the physically based rendering techniques. This model aims to accurately represent the subtle variations in real‐world terrain surfaces and highlight the key elements of hill terrain. Theoretically, this model provides a foundation for the virtual reconstruction of real‐world terrain. [ABSTRACT FROM AUTHOR]

To navigate complex terrains, insects use diverse tarsal structures (adhesive pads, claws, spines) to reliably attach to and locomote across substrates. This includes surfaces of variable roughness and inclination, which often require reliable transitions from ambulatory to scansorial locomotion. Using bioinspired physical models as a means for comparative research, our study specifically focused on the diversity of tarsal spines, which facilitate locomotion via frictional engagement and shear force generation. For spine designs, we took inspiration from ground beetles (family: Carabidae), which is a largely terrestrial group known for their quick locomotion. Evaluating four different species, we found that the hind legs host linear rows of rigid spines along the entire tarsus. By taking morphometric measurements of the spines, we highlighted parameters of interest (e.g. spine angle and aspect ratio) in order to test their relationship to shear forces sustained during terrain interactions. We systematically evaluated these parameters using spines cut from stainless steel shim attached to a small acrylic sled loaded with various weights. The sled was placed on 3D-printed models of rough terrain, randomly generated using fractal Brownian motion, while a motorized pulley system applied force to the spines. A force sensor measured the reaction force on the terrain, recording shear force before failure occurred. Initial shear tests highlighted the importance of spine angle, with bioinspired anisotropic designs producing higher shear forces. Using these data, we placed the best (50° angle) and worst (90° angle) performing spines on the legs of our insect-scale ambulatory robot physical model. We then tested the robot on various surfaces at 0°, 10°, and 20° inclines, seeing similar success with the more bioinspired spines. [ABSTRACT FROM AUTHOR]

Aim: In continuous environments, niche limits of species often determine their distribution limits. However, when these limits spatially coincide with a perceived dispersal barrier, the determinants of species' ranges may be confounded. We investigate the distribution pattern of the Brown Hornbill (Aves: Bucerotidae), which spans significant riverine barriers, but stops south of the Brahmaputra River. Considering its preference for low‐elevation evergreen forests, we posit that the lack of sufficient habitats north of the Brahmaputra prevents dispersal of the Brown Hornbill, and not the river itself. Location: The Brahmaputra valley and the Indo‐Burma hotspot. Taxa: Anorrhinus austeni, Aceros nipalensis. Methods: We analysed citizen‐science occurrence data on hornbill presence against a suite of climatic, canopy‐specific and topographical predictors to model the environmental niche of the Brown Hornbill. We used presence‐only maximum entropy modelling in an information theoretic framework, in conjunction with constructing binary logistic regression models using presence and pseudoabsence data. We compared niche models of the Brown Hornbill and the Rufous‐necked hornbill, a close relative with a similar distribution, but which has spanned the Brahmaputra River. Results: Despite the presence of suitable wet‐evergreen forests north of the Brahmaputra River, the hilly terrains in the region act as a biogeographic barrier for the Brown Hornbill, which prefers lowland evergreen forests. Further, highly suitable regions for the Brown and the Rufous‐necked Hornbills precisely delineate low and high‐elevation evergreen forests respectively, indicating that these birds are separated along an elevational axis. Main Conclusions: The Brahmaputra River lies at the cusp of two subtly different environmental regimes. It may therefore serve as the niche limit for certain organisms and not as a physical obstacle to their dispersal. Our study implicitly predicts how widespread deforestation prevalent in the lowland evergreen forests of this region adversely impacts the distributions of organisms that depend on them, such as the Brown Hornbill. Further, our study proffers an approach to ascertain determinants of species distributions in a hypothesis testing framework. [ABSTRACT FROM AUTHOR]

The traversability of the terrain ahead of planetary rovers significantly impacts the success of their extraterrestrial exploration missions. Accurate perception of the terrain force through a forward wheel-on-limb detection system can provide crucial data for assessing the traversability of the terrain ahead. Existing constant force control methods, largely based on static terra mechanics models, struggle to meet the operational needs of planetary rovers in unknown environments. To address this issue, this paper proposes an improved adaptive impedance control method for robotic arms in unknown soft terrain. First, a dynamic soft terrain model is established to simulate the unknown soft terrain environment. Based on this model, an improved adaptive impedance control method is designed, and its stability is rigorously proven. The proposed method is then comprehensively validated through simulation experiments, hardware-in-the-loop tests, and experiments using a planetary rover model in flat, inclined, and curved terrain scenarios. Experimental results demonstrate the effectiveness of the designed adaptive impedance control method. [ABSTRACT FROM AUTHOR]

This study aims to evaluate the feasibility of using non-contact data collection methods—specifically, UAV (unmanned aerial vehicle)-based and terrestrial laser scanning technologies—to assess forest stand passability, which is crucial for military operations. The research was conducted in a mixed forest stand in the Březina military training area, where the position of trees and their DBHs (Diameter Breast Heights) were recorded. The study compared the effectiveness of different methods, including UAV RGB imaging, UAV-LiDAR, and handheld mobile laser scanning (HMLS), in detecting tree positions and estimating DBH. The results indicate that HMLS data provided the highest number of detected trees and the most accurate positioning relative to the reference measurements. UAV-LiDAR showed better tree detection compared to UAV RGB imaging, though both aerial methods struggled with canopy penetration in densely structured forests. The study also found significant variability in DBH estimation, especially in complex forest stands, highlighting the challenges of accurate tree detection in diverse environments. The findings suggest that while current non-contact methods show promise, further refinement and integration of data sources are necessary to improve their applicability for assessing forest passability in military or rescue contexts. [ABSTRACT FROM AUTHOR]

Significant uncertainty exists both on the supply and demand side of food bank operations, but the demand side has received significantly less attention in the literature. In this study, we used data from a food bank branch to characterize the behavior of their partner agencies. Of the three predictive models considered, random forest was applied to estimate the number of persons served by the partner agencies, which is then converted to food demand. Our bottom-up approach can be replicated for all branches in a food bank's network to obtain their true demand. [ABSTRACT FROM AUTHOR]

Measurement is significant in geospatial thinking research. This study evaluated the geospatial thinking of pre-service geography teachers through an orienteering design tasks and subjective evaluation methods. Moreover, it analyzed their level of geospatial thinking, difficulty of evaluation indicators and rater severity with the Many-Faceted Rasch Model. The results showed the geospatial thinking of participating pre-service geography teachers was at a moderate level. The estimation results of the model were well fitted. The evaluation indicators had a good degree of differentiation. Therefore, using the MFRM helps interpret the results of a geospatial thinking assessment in a more comprehensive way. [ABSTRACT FROM AUTHOR]

Relief mapping through dense tropical forest is a challenge, which can be met by processing radar images. Various global digital elevation models (DEMs) are available, either free or paid, the most widely used methods for producing them at global scale being photogrammetry and short wavelength radar interferometry. However, the resulting DEMs do not directly represent the ground surface but rather the canopy or something in between. Since most users need terrain models for a variety of applications in geoscience, water management etc. but use such canopy models for lack of anything better, it was relevant to assess the consequences of this inappropriate but unavoidable choice. This paper presents a study carried out in the Brazilian Amazon, where an airborne P-band radar interferometric DEM was used as a reference based on its accuracy assessed in previous studies. A well-established DEM (SRTM) and newer ones (Copernicus and Fabdem), as well as a DEM obtained from aerial X-bands radar data, were compared to the reference according to different criteria related to elevation (accuracy and a typical application, i.e., dam filling simulation) and to slope at different scales (accuracy and a typical application, i.e., terrain classification). The results highlighted the risks of using short wavelengths to represent the terrain and emphasized the importance of slope in different resolution scales. [ABSTRACT FROM AUTHOR]

Ticks represent important vectors of a number of bacterial and viral disease agents, owing to their hematophagous nature and their questing behavior (the process in which they seek new hosts). Questing activity is notably seasonal with spatiotemporal dynamics that needs to be understood in detail as part of mediating and mitigating tick-borne disease risk. Models of the geography of tick questing activity developed to date, however, have ignored the temporal dimensions of that behavior; more fundamentally, they have often not considered the sampling underlying available occurrence data. Here, we have addressed these shortfalls for Amblyomma americanum, the most commonly encountered tick in the central Great Plains, via (1) detailed, longitudinal sampling to characterize the spatiotemporal dimensions of tick questing activity; (2) randomization tests to establish in which environmental dimensions a species is manifesting selective use; and (3) modeling methods that include both presence data and absence data, taking fullest advantage of the information available in the data resource. The outcome was a detailed picture of geographic and temporal variation in suitability for the species through the two-year course of this study. Such models that take full advantage of available information will be crucial in understanding the risk of tick-borne disease into the future. [ABSTRACT FROM AUTHOR]

Boundary recognition visually delineates the horizontal extent of subsurface anomalies, providing a foundational basis for interpreting gravity data. Compared to gravity observations, gravity gradient data offers the benefits of multiple components and the enhancement of shortwave information in graphical representations. In our study, we investigated the delineation of geological structure boundaries in the lunar Oceanus Procellarum region using gravity gradient techniques. First, based on the observations from the Gravity Recovery and Interior Laboratory (GRAIL) mission, we obtained high-precision synthetic full tensor values of the gravity gradient as our primary research data. Moreover, to reduce curvature errors during large-scale terrain forward modeling, we applied the spherical prism of tesseroid to eliminate the effects of near-surface topography. Grounded on the Bouguer anomalies of gravity gradient, four distinct boundary recognition methods have been employed to explore the geological structure of the lunar Oceanus Procellarum region. It includes the Theta map method, the directional Theta map method, the combination of total horizontal derivative and the modulus of full tensor gravity gradient, and the improved edge detection method based on the Theta map method.The common feature of these methods is the utilization of the multi-component data combination from gravity gradient tensors, which can enhance the accuracy of edge detection. From the investigation results presented in the paper, we have found the following: 1) The improved edge detection method, based on the Theta map principle, enables better identification of the center of subsurface anomaly bodies during boundary recognition, utilizing a consistent single color in graphic displays. 2) According to the results of boundary recognition, numerous strip anomalies exist in the Oceanus Procellarum area that show less correlation with topographic relief. One possible explanation for these graphical anomalies of gravity gradient is that they serve as channels for the upwelling of mantle material during lunar evolution. [ABSTRACT FROM AUTHOR]

The Nari Caldera on Ulleung Island, an oceanic intraplate volcano, is a significant repository of information on the latest volcanic activity. To interpret the characteristics of the latest volcanic activity, it is essential to understand the caldera structures formed by the most explosive eruption of the Ulleung volcanic edifice. This study presents a three-dimensional (3-D) resistivity model based on audio-magnetotelluric (AMT) data and interprets the caldera structure of Ulleung Island. New land and ocean terrain models were used for the 3-D inversion of the AMT data, and a finer, nonuniform grid was generated for the caldera area. Subsequently, 3-D inversion and imaging were conducted on the AMT data at 25 stations. In the caldera area of Ulleung Island, the 3-D resistivity model is divided into two bodies: a high resistivity body located in the south and a low resistivity body located in the north. This structure is consistent with the location of the two calderas, Seongin Caldera and Nari Caldera, as inferred from geological studies. Furthermore, the high resistivity body located in the south exhibited a bowl shape in the 3-D space. Therefore, we suggest that the high resistivity body located south of the caldera on Ulleung Island is a structure of the Seongin Caldera. The Seongin Caldera has a diameter of approximately 1.5 km and a caldera fill height of approximately 0.8 km, as measured from the resistivity model. Based on the stratigraphy of Ulleung Island and the physical properties of the rock types, the interior of the Seongin Caldera was considered to have been filled with trachytic lavas of the Seonginbong Group. From the high geothermal gradient of Ulleung Island and the stratigraphy of the GH-4 borehole, the low resistivity body extending from the shallow depths of the Nari Caldera to the lower part of the Seongin Caldera could be interpreted as trachytic rocks that underwent hydrothermal alteration. In addition, a low resistivity body contains highly porous and/or weathered rock. This study presents information on the calderas of Ulleung Island that can aid in interpreting the characteristics of the latest volcanic activity. We expect this information to contribute to the preparation for potential volcanic hazards. [ABSTRACT FROM AUTHOR]

Objectives: In 3 dimensional (3D) scenes, the bottom surface of 3D models is usually horizontal, and terrain models are mostly irregular surfaces with continuous undulations, which leads to problems such as gaps or terrain flooding when matching 3D models with terrain models, etc. In the current research on matching 3D models with terrain, there is a lack of scene complexity constraint, so some matching methods are not applicable to 3D scenes with multiple complexities. Therefore, how to complete mode terrain matching while taking into account the scene complexity constraint and retaining certain terrain integrity is the current problem to be considered and solved. Methods: Through research on 3D model data organization, scene complexity evaluation, and grid reconstruction, etc., a 3D model and terrain model matching method that takes into account the scene complexity is proposed to achieve a seamless matching between the model and the terrain. First, the method improves the data organization of multi-source heterogeneous 3D model by combining the characteristics of 3D model. Then, it constructs the 3D scene complexity evaluation model by combining the scene complexity evaluation factors, and calculates and classifies the 3D scene complexity level. Finally, according to the scene complexity classification results, it realizes the accurate matching of multi-source heterogeneous 3D models and terrain considering the scene complexity. Results: Different 3D scenes contain different amounts of information, and their corresponding scene complexity is also different. For the 3D scenes with low scene complexity, the elevation value fitting method is used, and the 3D scenes with high scene complexity use the grid reconstruction method. The visual perception and realtime frame rate are used as reference indicators to compare and analyze this method with the traditional method. The frame rate is stable at 58 frames per second, and it can be seen that the scene complexity constraint of this method can optimize the system loading smoothness and improve the visualization effect. Con⁃ clusion: We construct a scene complexity evaluation model based on scene information entropy, and design a model and terrain adaptation algorithm adapted to the scene complexity level. We organize and express the 3D scene model data with different scene complexity more efficiently while ensuring the real-time frame rate when rendering the 3D model, and effectively optimizes the problem of accurate matching between model and terrain. [ABSTRACT FROM AUTHOR]

In addition to geometric accuracy, topological information, appearance and georeferenced data, semantic capabilities are key strengths of digital 3D city models. This provides the foundation for a growing number of use cases, far beyond visualization. While these use cases mostly focused on models of buildings or the terrain so far, the increasing availability of data on roads and other transportation infrastructure opened up a range of emerging use cases in the field of semantic 3D streetspace models. While there are already a number of implemented examples, there is also a potential for new use cases not yet established in the field of 3D city modeling, which benefit from detailed representations of roads and their environment. To ensure clarity in our discussions, we introduce an unambiguous distinction between the terms 'application domain', 'use case', 'functionality' and 'software application'. Based on these definitions, use cases are categorized according to their primary application domain and discussed with respect to relevant literature and required functionalities. Furthermore, requirements of functionalities towards semantic 3D streetspace models are determined and evaluated in detail with regard to geometric, semantic, topological, temporal and visual aspects. This article aims to give an overview on use cases in the context of semantic 3D streetspace models and to present requirements of respective functionalities, in order to provide insight for researchers, municipalities, companies, data providers, mapping agencies and other stakeholders interested in creating and using a digital twin of the streetspace. [ABSTRACT FROM AUTHOR]

Terrain analysis plays an important role in computer graphics, hydrology and geomorphology. In particular, analyzing the path of material flow over a terrain with consideration of local depressions is a precursor to many further tasks in erosion, river formation, and plant ecosystem simulation. For example, fluvial erosion simulation used in terrain modeling computes water discharge to repeatedly locate erosion channels for soil removal and transport. Despite its significance, traditional methods face performance constraints, limiting their broader applicability. In this paper, we propose a novel GPU flow routing algorithm that computes the water discharge in 풪(log n) iterations for a terrain with n vertices (assuming n processors). We also provide a depression routing algorithm to route the water out of local minima formed by depressions in the terrain, which converges in 풪(log2 n) iterations. Our implementation of these algorithms leads to a 5× speedup for flow routing and 34 × to 52 × speedup for depression routing compared to previous work on a 10242 terrain, enabling interactive control of terrain simulation. [ABSTRACT FROM AUTHOR]

The reproduced planetary boundary layer (PBL) wind is commonly applied in downscaled simulations using commercial CFD codes with Reynolds-averaged Navier–Stokes (RANS) turbulence modeling. When using the turbulent inlets calculated by numerical weather prediction models (NWP), adjustments of the turbulence eddy viscosity closures and wall function formulations are concerned with maintaining the fully developed wind profiles specified at the inlet of CFD domains. The impact of these related configurations is worth discussing in engineering applications, especially when commercial codes restrict the internal modifications. This study evaluates the numerical performances of open-source OpenFOAM 2.3.0 and commercial Fluent 17.2 codes as supplementary scientific comparisons. This contribution focuses on the modified turbulence closures to incorporate turbulent profiles produced from mesoscale PBL parameterizations and the modified wall treatments relating to the roughness length. The near-ground flow features are evaluated by selecting the flat terrains and the classical Askervein benchmark case. The improvement in near-ground wind flow under the downscaled framework shows satisfactory performance in the open-source CFD platform. This contributes to engineers realizing the micro-siting of wind turbines and quantifying terrain-induced speed-up phenomena under the scope of wind-resistant design. [ABSTRACT FROM AUTHOR]

While it is clear that space matters when it comes to explaining crime, there is a lack of research exploring whether such spatial effects remain significant if victim characteristics are introduced into the analysis. This paper addresses this gap by applying risk terrain modeling to predict assaults for victims of different racial backgrounds and local versus non-local residents. The results show spatial risk factors have different impact on the likelihood of victimization for individuals of different racial backgrounds and those who live in the city where the crime occurs versus those who are visiting. The findings suggest that more attention should be given to integrating the victim characteristics with the characteristics of space when predicting violent crime using spatial analysis. [ABSTRACT FROM AUTHOR]

The increasing role of unmanned aerial vehicle (UAV) swarms in modern warfare poses a significant challenge to ground and air defense systems. Considering complex terrain environments and multi-sensor resources including radar and photoelectric systems constraints, a novel multi-sensor dynamic scheduling algorithm is proposed in this paper. Firstly, a transmission model with Fresnel zone under complex terrain and sensor models for radar/photoelectric systems are established. Considering the constraints of 6 factors, such as pitch angle, array scanning angle and threat levels, a detection model is developed subsequently. Secondly, to meet the real-time requirements of ground and air defense systems, a fast calculation method for Fresnel zone clearance using adaptive buffer is achieved. Thirdly, an improved Hungarian algorithm is proposed to solve the combinatorial optimization problem of sensor scheduling. Finally, simulation experiments are conducted to evaluate the algorithm performance under different conditions. The results demonstrate that the proposed approach significantly reduces the sensor switching rate while achieving a high sensor-UAV matching rate and high-threat matching rate. Furthermore, the simulation results verify the effectiveness of the proposed algorithm when applied to multi-sensor scheduling for defending UAV swarms. • Modern militaries need advanced sensor scheduling solutions to defend UAV. • Considering the Fresnel zone under complex terrain is crucial. • Adaptive buffer enables rapid calculation of Fresnel zone clearance. • Multiple sensor constraints make scheduling problems realistic. • Improved algorithm effectively enhances matching rates while reducing switching. [ABSTRACT FROM AUTHOR]

Flood hazard assessment of cities gained significance globally due to rise in frequency of flood events and rapid urbanisation. Uncertainties in flood inundation models largely depend on the quality of input datasets, among which topography plays a vital role. This study demonstrates the effectiveness of global terrain models in simulating accurate flooding and generating hazard maps while considering the influence of land-use dynamics focussing on data-scarce regions. Open-source forest and building removed digital elevation model (FABDEM) and a terrain model TDX-12 DTM derived from TanDEM-X 12 DSM using a simple-morphological-filtering technique are considered for comparing their performance in simulating a flood event occurred in Surat city during the year 2006. Spatially varying short-term urban growth scenario for the year 2035 is developed by utilizing historical land-use maps of the study area and urban growth indicators. These are combined using multi-criteria-decision-making techniques and Cellular-Automata-Markov-Chain model. The FABDEM based hydrodynamic model performed better (Root-Mean-Squared-Error RMSE of 1.59 m) than TDX-12 DTM based model (RMSE: 1.88 m). Intercomparison of hazard maps of the FABDEM and TDX-12 DTM with ground-surveyed TopoDEM based-model showed an overall accuracy of 71.8% and 72.8%; for the future scenario 71.8% and 75.5% respectively. In a span of 29 years, a notable increase in hazard magnitude of 7.5% is solely attributed to change in land dynamics. In this study, though the FABDEM based-model showed better RMSE than TDX-12 DTM, the model is relatively less successful in capturing high-hazard regions. The DEMs processed for removal of non-ground objects yield accurate models than globally trained models. [ABSTRACT FROM AUTHOR]

A major threat is posed to both human society and the environment by geological hazards, necessitating the need for accurate and comprehensive investigation and application in this field. Traditional geological hazard monitoring technologies are not only inefficient, but also fail to accurately delineate affected areas and provide comprehensive disaster data sets, preventing researchers from conducting precise investigations, detection and prevention. Therefore, this article summarizes the application of oblique photography technology through examples, especially its application in the investigation of local geological disasters such as debris flows and landslides, and the use of orthogonal-based corrected single-view oblique photography technology in the investigation of regional geological disasters such as earthquakes. Through summary, it is found that compared with traditional methods, UAV oblique photography technology is more excellent in the accuracy of investigation, detection, prevention, terrain information identification and geological disaster risk factor analysis in geological disasters. UAV oblique photography technology plays a vital role in the investigation, detection and prevention of geological disasters due to its versatility, efficiency and accuracy. It can help create detailed maps and 3D models of terrain by capturing high-resolution images from multiple angles to help identify potential geohazards. It can also detect changes in landforms, vegetation or waterways to provide warning signs for landslides, sinkholes or provide early warning signals for potential geological hazards such as erosion. High-resolution imagery captured by drones allows rescuers to quickly assess impacts on infrastructure, settlements and natural resources, thereby facilitating the efficient allocation of resources for rescue and recovery efforts. Drone oblique photography technology significantly enhances the ability to investigate, detect and prevent geohazards by providing timely, detailed spatial information critical for informed decision-making and proactive disaster management. Additionally considering that drone oblique photography technology may be combined with artificial intelligence algorithms and virtual reality (VR) imaging technology in the future, this integration is expected to improve the efficiency and accuracy of geological disaster management, thus ensuring the safety of human life and property. [ABSTRACT FROM AUTHOR]

I investigate the impact of economic integration, in terms of trade costs and information technology, on the interregional location patterns of retail firms by introducing three features into a new economic geography model: individuals consume only a restricted number of varieties; firms compete for customer acquisition; and the advancement of information technology enables consumers to easily perceive and buy products sold by firms in distant regions. I consider two possibilities regarding competition for attention: relative competition, in which a firm can attract more consumers by advertising more than the average level, and absolute competition, in which each firm competes with another to enhance the share of its advertisement in the market. I show that consumption variety is richer in the agglomerated region, but spatial consumption inequality shrinks as information technology develops. A reduction in trade costs may cause the redispersion of economic activities under sufficiently low trade costs only in the case of absolute competition. This is more likely when competition for attention is fierce and information technology is not fully developed. The results not only support the complementarity between information technology and cities but also provide a new insight that competition among firms is an important factor governing the impact of information technology on economic geography. [ABSTRACT FROM AUTHOR]

The Shuttle Radar Topography Mission (SRTM) is a digital representation of the terrain surface morphology that contains rich terrain information and is widely used in environmental analyses. However, SRTM is adversely affected by mixed noise, which typically include random and stripe noise. Mixed noise results in the significant loss of topographic information, which reduce the validity of related research. To eliminate mixed noise in SRTM data, we propose an adaptive low‐rank group sparse model based on edge preservation (ALGS_EP) to remove mixed noise from datasets. The method relies on a low‐rank group sparse model that considers the gradient features of the terrain. It calculates a terrain factor to adapt the noise elimination model to terrain changes. Additionally, it integrates with the edge structure of elevation data and applies a double‐gradient constraint to preserve the structural details of the elevation data. The proposed model, built upon the alternating direction multiplier method framework, enhances the traditional weighted kernel paradigm minimization algorithm by introducing variable weights that adjust according to the gradient of elevation data during iterations. Additionally, it incorporates the correlation between strip noise and residual data blocks when computing the iteration count, ensuring an iterative solution approach that converges to the optimal solution. We used ALGS_EP to process global SRTM 1 data and published a higher‐quality and higher‐precision elevation dataset. The elevation data noise before and after noise elimination were statistically analyzed. Simulated and empirical results show that the model is highly robust and more effective than existing methods in both visual and quantitative evaluations. The noise elimination rate was 97.6%, compared to the original data. Therefore, this research was valuable for applications that use digital elevation model as an important data layer. [ABSTRACT FROM AUTHOR]

The parabolic equation (PE) model is effective for predicting signal propagation over irregular terrains. The shift map method of the PE model is highly accurate and widely used for terrain propagation predictions. The maximum propagation angle is a crucial parameter of the shift map model. Here, unreasonable values may result in incorrect results or further computations. However, the prediction formula used in existing literature is limited, thereby leading to inaccurate results. In this study, a maximum propagation angle prediction method was developed. The maximum propagation angle with terrain should be calculated as the sum of the maximum propagation angle without terrain and twice the maximum terrain slope angle. Hence, the selection criteria for the different models in the hybrid terrain model were reanalyzed. An accurate maximum propagation angle is key for the PE model over irregular terrains, as it ensures prediction accuracy and improves calculation speed. [ABSTRACT FROM AUTHOR]

Seamless integration of three-dimensional (3D) terrain models and hydraulic structure models is a technical challenge in the construction of 3D virtual scenes for irrigation areas. This study proposes a level-of-detail (LOD)-based dynamic classification integration method for hydraulic structure models and 3D terrain models, called CM-D-LOD. Hydraulic structures are classified according to their point, line, and surface morphologies, as well as their dependence on or independence of the terrain into four categories: point-like hydraulic structures independent of terrain, line-like hydraulic structures dependent on terrain, surface-like hydraulic structures dependent on terrain, and surface-like hydraulic structures independent of terrain. By utilizing the proposed model classification integration method, a visualization management platform for virtual geographical environments of irrigation areas is developed, and experiments are conducted in the Zhuluo Ba Irrigation Area within the large economic zone along China's eastern coast. Results demonstrate that the integration accuracy can be controlled between 0.2 and 0.7 m and that the 3D virtual scene of the irrigation area can be updated in real time. The proposed classification integration method transforms the traditional global model integration approach into a more efficient one, significantly improving the efficiency of constructing virtual geographical scenes for irrigation areas. [ABSTRACT FROM AUTHOR]