Terrain Features Research Articles

Air Pollutant Spatiotemporal Evolution Characteristics and Effects on Human Health in Xi'an City

Xi'an is the political, economic, and cultural center of northwest China with a developed industry. Air pollution incidents have brought great challenges to the high-quality development of the social economy. It is vital to study air pollution characteristics and clarify their impact on human health. In this study, we first analyzed the spatiotemporal variations in air pollutants in the study region from 2015 to 2021. Then, the air quality index (AQI), aggregate air quality index (AAQI), and health risk-based air quality index (HAQI) were used to assess health risks. Based on these, the AirQ2.2.3 model was used to quantify health effects. The results showed that the major pollutants were PM10, PM2.5, and O3. The main pollution characteristics of the study area were terrain characteristics and the mixed pollution of anthropogenic emissions. Compared to that of AQI, AAQI and HAQI showed better classification performance for pollution levels. HAQI revealed that approximately 80 % of the population was exposed to unhealthy air throughout the year in the study region. People were most exposed to unhealthy air in winter, followed by autumn and spring, and the least in summer. The AirQ2.2.3 model quantified the total mortality proportions attributable to PM2.5, PM10, SO2, CO, NO2, and O3, which were 0.99 %, 2.04 %, 0.41 %, 1.72 %, 8.76 %, and 3.67 %, respectively. The attributable proportion of mortality of the respiratory system and cardiovascular diseases was consistent with the change rule of total mortality.

Smart City Wildfire Risk Analysis with Fuzzy Multi-Criteria Decision-Making

Uncontrolled wildfires pose a significant threat, potentially causing extensive damage to biodiversity, soil quality and human resources. It’s crucial to swiftly detect and predict these wildfires to minimize their catastrophic consequences. To address this, our research introduces a wildfire prediction model that ranks cities based on risk leveraging multi-criteria decision-making (MCDM) to systematically assess conflicting factors in decision-making. This model integrates wildfire risks into a city’s resilience strategy, utilizing fuzzy set theory to manage imprecise data and uncertainties. As part of this approach, we compile a new dataset encompassing weather patterns, vegetation types, terrain features and population density across various Californian cities. Ultimately, the model assesses and ranks the wildfire risk for each city in California.

Research on Artillery Target Size Determination Method Considering Ballistic and Terrain Characteristics

This study proposes a method for determining the optimal target size for an artillery range considering ballistics and environmental conditions. To this end, the size of the probable error of each type of ammunition and charge determined during shooting were considered, and the effect of the firing position and target terrain characteristics on the target size was analyzed. In conclusion, the size of the target increased as the range increased, and a larger target size was required for the DPICM than for the general high explosive. Accordingly, the optimal target size must be determined by considering various factors such as topographical characteristics, shooting position location, and shooting range safety standards.

Influence of Morphometric Relief Parameters on Soil Depth Changes and Humus Horizon Thickness in Relation to Erosion-Accumulation Processes: A Study in the Ipeľská Pahorkatina Hills, Slovakia

Abstract This study examines the spatial distribution of soil types and their susceptibility to erosion and accumulation processes in a study area in Slovakia. Field research involving 71 probes identified various soil types, with Regosols and Cutanic Luvisols being predominant. The study found that erosion-accumulation processes were detected in 69.97% of the probes, with changes observed in soil horizons. Soil analysis revealed different relations between soil depth, humus thickness, and terrain characteristics such as slope, slope length, and slope length and steepness factor (LS factor). Specifically, we confirmed a moderately strong positive correlation between soil depth and humus thickness (r = 0.597, n = 71, p < 0.001). Shallow soils (0–30 cm) exhibited a very strong positive correlation between soil depth and humus horizon thickness (r = 0.978, n = 33, p < 0.001). Conversely, no relationship was found in moderately deep soils (30–60 cm) (r = 0.018, n = 14, p < 0.948). For deep soils, we identified a moderately strong positive correlation (r = 0.345, n = 24, p = 0.098). While slope and slope length showed relationships with soil depth and humus thickness, the LS factor did not exhibit a clear correlation. These findings underscore the importance of understanding soil dynamics in informing land management practices, especially in areas susceptible to erosion. Recommendations include continued monitoring of eroded soils and implementing erosion control measures to maintain soil health and sustainability in agricultural production amidst climate change challenges.

KozhukhovaE.I., MustafinKh.Kh., SemenovV.A., UchanevaE.N. Reconstructing Ethnocultural Processes in the Sausken Valley (Tuva) during the Later Half of the 1st Millennium BC

The article represents findings of the group research devoted to reconstructing ethnocultural processes of the second half of the 1st millennium BC (Sausken Terrain Feature, Tuva). The research comprehensively studies local population groups who left behind their burial monuments in Eerbek River Valley. The study engages geneticist, ancient technologies experts, anthropologists and archaeologists. Kurgans of the middle and late Uyuk-Sagly Culture (5th–2nd century BC) were excavated in 2011–2016 by Tuva archaeological expedition arranged by the Russian Academy of Sciences Institute for the History of Material Culture. Nearly all burials in the kurgans had been robbed back in the ancient days. The study analyses above-ground structures and internal layouts of the kurgans, the burial ritual and tools (weapons, mirrors, belt accessories, bone items, embellishments). It allows to conclude that the kurgans have varying cultural attributions. The research also implies instrumental process analysis of glass and stone beads, chromatography of fabrics and proteomic analysis of archaeological leather. The paper reveals radiocarbon dating (14C) and correlations with other burial monuments of the Scythian Tuva. Craniological materials analysed allow to determine populations that could have been related to the aliens who arrived at the Eerbek River Valley in the Uyuk times. The Ozen-Ala-Belig groups differ from their peers from other territories in Tuva. It might be explained through the Uyuyk migrants mixing with the Eerbek Valley people who had been living there since the late Bronze Age. The paleogenetic study of 19 items especially focuses on researching the Y-chromosome DNA. For many researched samples the study determines haplogroups Q1b1a3-L330 and R1a1a1b2-Z93 (or its subgroups). The area of spread for these haplogroups is Soviet Central Asia, Central Asia, West and South Siberia. One samples is assigned to I2a1b1-M223 haplogroups with the area of spread in the Balkans and South-Eastern Europe. The obtained findings may indicate that the Western and Eastern nomads kept in touch during the researched period. The archaeological, anthropological and genetic data lead to a conclusion that the Scythian cultures in the given region is complex and multi-faceted.

Геоинформационное моделирование рельефа для выявления перспективных участков археологических исследований на примере памятников Саратовской области

This article provides an overview of modern remote sensing techniques in archaeology and their practical applications. The widespread use of GIS technologies and remote sensing methods such as photogrammetry and laser scanning is a distinguishing characteristic of contemporary archaeology. Remote sensing data is employed not only for the analysis of 3D archaeological objects and territories but also in the digital terrain models (DTMs) analysis to search for and identify potential archaeological excavation sites. The introduction of remote sensing methods in archaeology has brought about a change in the approach to conducting archaeological studies. In the field of international research, a distinct stage known as predictive archaeology, which involves preliminary reconnaissance of an area before excavation, has emerged. The study is focused on the archaeological sites of Stantsiya Krasavka and Akhmatskoe Gorodishche, located in the Atkarsky and Krasnoarmeysky municipal districts of the Saratov region. The selected study areas applied the DTM analysis, specifically using the “Hillshade” technique (analytical shading relief), which allows for the detection of previously overlooked terrain features. Based on the results, the potential of this technology for identifying individual archaeological objects using contemporary open DTMs and field geodetic survey data was analyzed. Experimental determination of the optimal DTM resolution for the identification and analysis of objects was conducted in areas previously subject to archaeological research. The experiments and comparative analysis of various laser scanning technologies led to the identification of optimal methods and filtering parameters to “exclude” vegetation and generate DTMs.

Applicability Study of Euler–Lagrange Integration Scheme in Constructing Small-Scale Atmospheric Dynamics Models

The atmospheric flow field and weather processes exhibit complex and variable characteristics at small scales, involving interactions between terrain features and atmospheric physics. To investigate the mechanisms of these process further, this study employs a Lagrangian particle motion model combined with a Euler background field approach to construct a small-scale atmospheric flow field model. The model streamlines the modeling process by combining the benefits of the Lagrangian dynamics model and the Eulerian integration scheme. To verify the effectiveness of the Euler–Lagrange hybrid model, experiments using the Fluent wind field model were conducted for comparison. The results show that both models have their advantages in handling terrain-induced wind fields. The Fluent model excels in simulating the general characteristics of wind fields under specific terrain, while the Euler–Lagrange hybrid model is better at capturing the upstream and downstream disturbances of the terrain on the atmospheric flow field. These findings provide powerful tools for in-depth diagnostic analysis of atmospheric flow simulation and convective precipitation processes. Notably, the Euler–Lagrange hybrid model demonstrates excellent computational efficiency, with an average computation time of approximately 2 s per time step in a Python environment, enabling rapid simulation of 40 time steps within approximately 90 s.

Relationships between Terrain Features and Forecasting Errors of Surface Wind Speeds in a Mesoscale Numerical Weather Prediction Model

Relationships between Terrain Features and Forecasting Errors of Surface Wind Speeds in a Mesoscale Numerical Weather Prediction Model

Analysis of the relationship between forest patch degradation, land uses, and terrain variables in a protected landscape in Ghana

Due to the implications of deforestation on global climate and environmental changes, concerns for forest patch degradation have grown in recent times. Recent studies have concentrated on assessing general land use and land cover changes, but in this study, we are interested in modeling the association between degraded patches of forest cover, land uses, and landscape's terrain characteristics. We use methods from landscape ecology, remote sensing, geo-information systems, and statistics to predict the association between degraded forest patches and related factors. Our findings show that forest patches within 1 km from agricultural land use are more likely to be degraded (AOR = 1.42, C.I. = 1.141–1.760, p = 0.002). Similarly, our results further reveal that forest patches within 1 km from human settlement footprints have a higher likelihood of being degraded (AOR = 1.38, C.I. = 1.083–1.768, p = 0.009). Our results demonstrate a need for the creation and enforcement of sound land use planning and management strategies that would adequately protect forest patches from the influence of agricultural land use and human settlement development.

Complexity, depoliticisation, and African nuclear ordering agency: a meso-level exploration

The regional nuclear ordering terrain in Africa is increasingly complex, with proliferating and deepening institutional relationships to the institutions of the global nuclear order. Applying a ‘complexity lens’ to this regional institutional apparatus may therefore seem like an intuitive way to understand its role in global nuclear ordering at large, and Africa’s place within it. However, one important concern when thinking about complex multinational regimes is depoliticisation. This has been examined in contexts of global development as well as nuclear order and we show this as a key feature of meso nuclear ordering in Africa. A complexity lens is useful to analyse the characteristics of the African regional institutional terrain. However, a complexity lens can perpetuate this depoliticisation if it does not acknowledge the political thrusts which underlie conceptions of ‘order’ and ‘disorder’.

Terrain-Aware Military Planning Agents

Terrain is important, often decisive, in military battles. This concept recurs across numerous historical examples, military theorists, and doctrinal manuals used by armies around the world. This poster discusses how Mission Command Agents, automated agents used to represent the behaviors of military forces in combat simulations, achieve this understanding of the importance of terrain. First, a review of tactical manuals from different nations consistently identified the importance of terrain to observation, fires, and mobility. Based on the unit’s mission, the geography afforded, or prevented, these activities with respect to the enemy force. The next step was to build an automated tool that could quickly calculate these effects based on the anticipated positions. The third step was to formulate these quantifiable terrain effects as objectives in a multi-objective search heuristic so that different places could be compared with each other. After excellent results with these techniques in a realistic military planning scenario, the team is further enhancing human-machine collaboration in this area by adding geospatial and military characteristics of terrain to an existing standard for Command and Control – Simulation Interoperation (C2SIM). With this enhancement, the reasoning employed by the agents is more explainable to military observers. It also allows military experts to adjust agent behaviors by adjusting their goals, without the need to change source code, while employing a user interface that they will be familiar with.

Clustering of Wind Speed Time Series as a Tool for Wind Farm Diagnosis

In several industrial fields, environmental and operational data are acquired with numerous purposes, potentially generating a huge quantity of data containing valuable information for management actions. This work proposes a methodology for clustering time series based on the K-medoids algorithm using a convex combination of different time series correlation metrics, the COMB distance. The multidimensional scaling procedure is used to enhance the visualization of the clustering results, and a matrix plot display is proposed as an efficient visualization tool to interpret the COMB distance components. This is a general-purpose methodology that is intended to ease time series interpretation; however, due to the relevance of the field, this study explores the clustering of time series judiciously collected from data of a wind farm located on a complex terrain. Using the COMB distance for wind speed time bands, clustering exposes operational similarities and dissimilarities among neighboring turbines which are influenced by the turbines’ relative positions and terrain features and regarding the direction of oncoming wind. In a significant number of cases, clustering does not coincide with the natural geographic grouping of the turbines. A novel representation of the contributing distances—the COMB distance matrix plot—provides a quick way to compare pairs of time bands (turbines) regarding various features.

Recognition of Plastic Film in Terrain-Fragmented Areas Based on Drone Visible Light Images

In order to meet the growing demand for food and achieve food security development goals, contemporary agriculture increasingly depends on plastic coverings such as agricultural plastic films. The remote sensing-based identification of these plastic films has gradually become a necessary tool for agricultural production management and soil pollution prevention. Addressing the challenges posed by the complex terrain and fragmented land parcels in karst mountainous regions, as well as the frequent presence of cloudy and foggy weather conditions, the extraction efficacy of mulching films is compromised. This study utilized a DJI Mavic 2 Pro UAV to capture visible light images in an area with complex terrain features such as peaks and valleys. A plastic film sample dataset was constructed, and the U-Net deep learning model parameters integrated into ArcGIS Pro were continuously modified and optimized to achieve precise plastic film identification. The results are as follows: (1) Sample quantity significantly affects recognition performance. When the sample size is 800, the accuracy of plastic film extraction notably improves, with area accuracy reaching 91%, a patch quantity accuracy of 96.38%, and an IOU and F1-score of 85.89% and 94.20%, respectively, compared to the precision achieved with a sample size of 300; (2) Different learning rates, batch sizes, and iteration numbers have a certain impact on the training effectiveness of the U-Net model. The most suitable model parameters improved the training effectiveness, with the highest training accuracy achieved at a learning rate of 0.001, a batch size of 10, and 25 iterations; (3) Comparative experiments with the Support Vector Machine (SVM) model validate the suitability of U-Net model parameters and sample datasets for precise identification in rugged terrains with fragmented spatial distribution, particularly in karst mountainous regions. This underscores the applicability of the U-Net model in recognizing plastic film coverings in karst mountainous regions, offering valuable insights for agricultural environmental health assessment and green planting management in farmlands.

Pre- and post-fire forest canopy height mapping in Southeast Australia through the integration of multi-temporal GEDI data, satellite images, and Convolution Neural Network

ABSTRACT This study leveraged Convolutional Neural Network (CNN) models to estimate canopy height in Southeast Australian forests before and after the 2019–2020 bushfire event, using inputs from Sentinel-1, Sentinel-2, spectral indices, and terrain features and GEDI canopy height (rh99) as target variables. Our research consisted of three primary objectives: (1) an evaluation of GEDI rh99 canopy height in relation to an nDSM derived from airborne LiDAR, (2) the development of two CNN models for pre- and post-fire scenarios, and (3) utilizing the trained CNN models to generate pre- and post-fire canopy height maps and canopy height change maps across the continuous landscape. GEDI rh99 accuracy analysis revealed R 2 = 0.85, RMSE = 7.25 m and nRMSE = 0.23 for pre-fire GEDI and R 2 = 0.78, RMSE = 10.1 m and nRMSE = 0.3 for post-fire GEDI. The pre-fire CNN model achieved R 2 = 0.75, RMSE = 7.29 m and nRMSE = 0.22, and post-fire CNN model achieved R 2 = 0.62, RMSE = 11.67 m, and nRMSE = 0.34 when validated against nDSM. Bias analysis was conducted, revealing that GEDI rh99 underestimated nDSM across all canopy height ranges, and slope has no significant impact on GEDI accuracy. Notably, post-fire GEDI rh99 exhibited significant underestimation in high to extreme fire severity. The CNN models’ prediction displayed overestimation in short forests < 20 m and increasing underestimation in taller forests > 20 m and underestimation in high and extreme fire severity. Subsequent investigation indicated that canopy layer thinning in high fire severity conditions resulted in weak GEDI waveform signals and consequent bias. Additionally, the GEDI rh99 bias was propagated to the CNN models, together with insensitivity of spectral band values to canopy height in dense and burnt forests potentially contributed to models’ bias. Finally, we demonstrated CNN models’ ability to monitor forest recovery by generating canopy height maps spanning East Gippsland from 2019 to 2023.

Open source robot localization for nonplanar environments

AbstractThe operational environments in which a mobile robot executes its missions often exhibit nonflat terrain characteristics, encompassing outdoor and indoor settings featuring ramps and slopes. In such scenarios, the conventional methodologies employed for localization encounter novel challenges and limitations. This study delineates a localization framework incorporating ground elevation and incline considerations, deviating from traditional two‐dimensional localization paradigms that may falter in such contexts. In our proposed approach, the map encompasses elevation and spatial occupancy information, employing Gridmaps and Octomaps. At the same time, the perception model is designed to accommodate the robot's inclined orientation and the potential presence of ground as an obstacle, besides usual structural and dynamic obstacles. We provide an implementation of our approach fully working with Nav2, ready to replace the baseline Adaptative Monte Carlo Localization (AMCL) approach when the robot is in nonplanar environments. Our methodology was rigorously tested in both simulated environments and through practical application on actual robots, including the Tiago and Summit XL models, across various settings ranging from indoor and outdoor to flat and uneven terrains. Demonstrating exceptional precision, our approach yielded error margins below 10 cm and 0.05 radians in indoor settings and less than 1.0 m in extensive outdoor routes. While our results exhibit a slight improvement over AMCL in indoor environments, the enhancement in performance is significantly more pronounced when compared to three‐dimensional simultaneous localization and mapping algorithms. This underscores the considerable robustness and efficiency of our approach, positioning it as an effective strategy for mobile robots tasked with navigating expansive and intricate indoor/outdoor environments.

A low-cost approach to map natural groundwater recharge potential zones of a tropical watershed with complex land occupation

Groundwater recharge is one of the components of the water cycle and is the most difficult parameter to estimate due to multiple factors that can influence the water percolation until it reaches the water table, such as land use/cover, slope, and soil texture, besides climatic conditions. Given that groundwater is Brazil's most widely exploited natural resource and its significance in sustaining ecosystems, apart from its role in guaranteeing the city's water security, mapping zones of recharge potential is a growing demand for water resources management. This work uses the GIS approach and the water balance (2010–2019) to map the groundwater recharge potential zones of the Adamantina Aquifer in Urânia (São Paulo State, Brazil) delimited on a local scale by the Córrego Comprido watershed. The results showed that 39% of the area has a recharge potential of 165 mm/year or 13% of the average annual precipitation (APPT) for the time, reflecting the gently undulating topography (2–7% slope) with sandy soil texture and shrubby herbaceous cover. Maintaining these same terrain characteristics and changing the land use/cover only, the presence of eucalyptus trees would decrease the recharge potential by 32% (111 mm/y; 9% of average APPT). As another example, the urban occupations would cause a reduction of 80% for the same parameter (33 mm/y; 3% of average APPT). Furthermore, in urban areas, a developing recharge potential value of only 7 mm/year (1% of average APPT) corresponding to sandy lands but with slopes of 7% was observed, demonstrating the primary control of land use/cover and topography in the estimates. The predominant recharge potential value of 165 mm/y correlated with the results of previous recharge studies conducted in Urânia. It demonstrated the versatility of this approach to extrapolate over large areas when required.

JPSSL: SAR Terrain Classification Based on Jigsaw Puzzles and FC-CRF

Effective features play an important role in synthetic aperture radar (SAR) image interpretation. However, since SAR images contain a variety of terrain types, it is not easy to extract effective features of different terrains from SAR images. Deep learning methods require a large amount of labeled data, but the difficulty of SAR image annotation limits the performance of deep learning models. SAR images have inevitable geometric distortion and coherence speckle noise, which makes it difficult to extract effective features from SAR images. If effective semantic context features cannot be learned for SAR images, the extracted features struggle to distinguish different terrain categories. Some existing terrain classification methods are very limited and can only be applied to some specified SAR images. To solve these problems, a jigsaw puzzle self-supervised learning (JPSSL) framework is proposed. The framework comprises a jigsaw puzzle pretext task and a terrain classification downstream task. In the pretext task, the information in the SAR image is learned by completing the SAR image jigsaw puzzle to extract effective features. The terrain classification downstream task is trained using only a small number of labeled data. Finally, fully connected conditional random field processing is performed to eliminate noise points and obtain a high-quality terrain classification result. Experimental results on three large-scene high-resolution SAR images confirm the effectiveness and generalization of our method. Compared with the supervised methods, the features learned in JPSSL are highly discriminative, and the JPSSL achieves good classification accuracy when using only a small amount of labeled data.

Region‐dependent meteorological conditions of thunderstorm based on clustering analysis of stability and precipitable water indices

AbstractThunderstorms contribute to a large number of destructive weather events. However, due to the availability of comprehensive thunderstorm datasets, current researches on thresholds of stability indices during thunderstorms have always been limited within a specific local area during a relatively short time span. This study uses observations of thunderstorms data and ERA5 reanalysis data in summer (June–August) at 0600 and 1200 UTC from 1995 to 2019 to investigate the region‐dependent thresholds of four indices for indicating the occurrence of thunderstorm. The indices include the K index (K), the Showalter index (SI), convective available potential energy (CAPE), and precipitable water (PW). By applying the K‐means clustering method on the composite mean fields of four indices on thunderstorm days, China is objectively divided into three regions based on three stability indices (K, SI, and CAPE) and two regions based on PW index. Region‐dependent thresholds have been established for each index related to thunderstorms. When these thresholds are applied, accuracy rate of detecting thunderstorms exceeds 60% at 85% of the stations over China. The remarkable regional difference in the thresholds of the four indices are further attributed to the climatic backgrounds. Summer climatological means of surface potential temperature and surface relative humidity play a pivotal role in shaping the distinctive thresholds of K, CAPE, and PW across diverse regions, while surface potential temperature and topography are pivotal for the region‐dependent thresholds of SI. Based on the terrain characteristics and relevant summer climatology in each region, the thunderstorm thresholds can be potentially applied to regions with limited thunderstorm records but exhibiting similar climatic features beyond China. These findings would provide a reliable reference for thunderstorm monitoring by meteorological departments and related decision‐making services.

Monitoring of spatio-temporal glaciers dynamics in Bhagirathi Basin, Gharhwal Himalayas using remote sensing data

Glacier retreat represents a highly sensitive indicator of climate change and global warming. Therefore, timely mapping and monitoring of glacier dynamics is strategic for water budget forecasting and sustainable management of water resources. In this study, Landsat satellite images of 2000 and 2015 have been used to estimate area extent variations in 29 glaciers of the Bhagirathi basin, Garhwali Himalayas. ASTER DEM has been used for extraction of glacier terrain features, such as elevation, slope, area, etc. It is observed from the analysis that Bhagirathi sub-basin has a maximum glaciated area of ~ 35% and Pilang has the least with ~ 3.2%, whereas Kaldi sub-basin has no glacier. In this region, out of 29 glaciers, 25 glaciers have shown retreat, while only 4 glaciers have shown advancement resulting in a total glacier area loss of ~ 0.5%, while the retreat rate varies from ~ 0.06 m/yr to ~ 19.4 m/yr. Dokarni glacier has maximum retreat rate (~ 19.4 m/yr), whereas Dehigad has maximum advancing rate (~ 10.1 m/yr). Glaciers retreat and advance have also been analyzed based on terrain parameters and observed that northern and southern orientations have shown retreat, whereas the area change is highly correlated with glacier length. The study covers more than 65% of the total glaciated area and based on the existing literature represents one of the most exhaustive studies to cover the highest number of glaciers in all sub-basins of the Bhagirathi basin.

Rock Slope Stability Analysis Using Terrestrial Photogrammetry and Virtual Reality on Ignimbritic Deposits.

Puerto de Cajas serves as a vital high-altitude passage in Ecuador, connecting the coastal region to the city of Cuenca. The stability of this rocky massif is carefully managed through the assessment of blocks and discontinuities, ensuring safe travel. This study presents a novel approach, employing rapid and cost-effective methods to evaluate an unexplored area within the protected expanse of Cajas. Using terrestrial photogrammetry and strategically positioned geomechanical stations along the slopes, we generated a detailed point cloud capturing elusive terrain features. We have used terrestrial photogrammetry for digitalization of the slope. Validation of the collected data was achieved by comparing directional data from Cloud Compare software with manual readings using a digital compass integrated in a phone at control points. The analysis encompasses three slopes, employing the SMR, Q-slope, and kinematic methodologies. Results from the SMR system closely align with kinematic analysis, indicating satisfactory slope quality. Nonetheless, continued vigilance in stability control remains imperative for ensuring road safety and preserving the site's integrity. Moreover, this research lays the groundwork for the creation of a publicly accessible 3D repository, enhancing visualization capabilities through Google Virtual Reality. This initiative not only aids in replicating the findings but also facilitates access to an augmented reality environment, thereby fostering collaborative research endeavors.