Elevation Map Research Articles

Semantic Labeling of High-Resolution Images Using EfficientUNets and Transformers

Semantic segmentation necessitates approaches that learn high-level characteristics while dealing with enormous quantities of data. Convolutional neural networks (CNNs) can learn unique and adaptive features to achieve this aim. However, due to the large size and high spatial resolution of remote sensing images, these networks cannot efficiently analyze an entire scene. Recently, deep transformers have proven their capability to record global interactions between different objects in the image. In this paper, we propose a new segmentation model that combines convolutional neural networks with transformers, and show that this mixture of local and global feature extraction techniques provides significant advantages in remote sensing segmentation. In addition, the proposed model includes two fusion layers that are designed to efficiently represent multimodal inputs and output of the network. The input fusion layer extracts feature maps summarizing the relationship between image content and elevation maps (DSM). The output fusion layer uses a novel multitask segmentation strategy where class labels are identified using class-specific feature extraction layers and loss functions. Finally, a fast-marching method is used to convert unidentified class labels to their closest known neighbors. Our results demonstrate that the proposed method improves segmentation accuracy compared to state-of-the-art techniques.

Management of Post-LASIK Ectasia.

Corneal ectasia is a sight-threatening complication of corneal refractive surgery characterized by progressive steepening and thinning of the cornea and subsequent loss of best-corrected visual acuity. To report the clinical outcomes following treatment of post-laser in situ keratomileusis (LASIK) induced ectasia. This is a retrospective case series of 7 patients (10 eyes) which developed post-LASIK ectasia. In these cases of postoperative ectasia, the presented clinical signs were either forme fruste keratoconus, thin cornea, posterior elevation map value > +15.0µm, or residual stromal bed < 300µm. All cases were treated with either collagen crosslinking (CXL) alone or combined with PRK or CXL and phakic intraocular implant using the Dresden protocol and a slight modification thereof. In all cases, the flap was created using the Moria M2 mechanical microkeratome (average flap thickness 118.15±12.88µm), and refractive error was corrected using the Wavelight Allegretto excimer laser. Average preoperative corrected visual acuity (CDVA) was 0.75 (±0.26) Snellen. Postoperative CDVA significantly increased to 0.86 (±0.13) Snellen (p=0.04, paired t-test). One eye lost three lines of its baseline CDVA (before ectasia), while all other eyes regained lines of CDVA. All cases remained stable during the follow-up. Several surgical procedures are used for the management of corneal ectasia. However, the best surgical approach should be determined based on the state of progression of the disease. Although ectasia remains a potentially devastating complication after refractive surgery, most patients can regain functional visual acuity with appropriate management, and corneal transplantation is infrequently indicated.

Strawberry yield monitoring based on a convolutional neural network using high-resolution aerial orthoimages

This article presents the results of studies comparing the quality of work of two modern models of convolutional neural networks YOLOv7 and YOLOv8 used to monitor the yield of strawberries. To do this, we used the transfer method of machine learning models on a set of collected data consisting of four classes of development of generative formations of strawberry. As a result of the study, we obtained a data set that contained images of flowers, ovaries, mature and not mature berries. To ensure the balance of classes in the dataset, the Oversampling method was used, which included the generation of new images by applying various operations, such as resizing the image, normalizing brightness and contrast, converting images by rotating them by a certain angle and reflection, random noise addition, Gaussian blur. To collect data (images) in the field, a DJI Phantom 2 quadrocopter with a DJI Zenmuse Gimbal suspension and a GoPro HD HERO3 camera was used. To assess the quality of the YOLOv7 and YOLOv8 models when recognizing specified classes, well-known metrics were used that estimate the proportion of objects found that are really objects of a given class, such as Precision, Recall and mAP. Analysis of the results showed that the mAP metric for all classes of the YOLOv7 convolutional neural network model was 0,6, and the YOLOv8 model was 0,762. Analysis of the test sample images showed that the average absolute percentage error of image recognition of all classes by the YOLOv7 and YOLOv8 models was 9,2%. The most difficult to recognize was class the ovary of strawberries, the average absolute percentage error of which was 13,2%. In further studies, the use of high-resolution stereo cameras is recommended, which will further improve the accuracy of monitoring potential yields due to the possibility of determining the dimensional parameters of strawberry fruits and constructing 3D models of elevation maps using photogrammetry.

Using Lidar Digital Elevation Models for Reflectometry Land Applications

Lidar elevation maps are utilized in a bistatic electromagnetic scattering model of the Cyclone Global Navigation Satellite System over San Luis Valley in Colorado, USA. The results are compared with results generated using the Shuttle Radar Topography Mission (SRTM) 1 arcsecond global digital elevation model (DEM). The high resolution lidar maps are also used to generate maps of surface roughness at length scales finer than the horizontal resolution of SRTM. Model results using these maps illustrate the importance of the spatial variation of surface roughness on reflectometry signals. Results also highlight the shortcomings of using SRTM in scattering models and show improvements when a lidar DEM is used.

Large Scale Topographic Mapping Using Stereo Pair Imagery from CAS500-1 Satellite

A CAS500-1 (Compact Advanced Satellite) is a high-resolution Earth-observing satellite launched in Mar 2021 and being operated by National Land Satellite Center at NGII (National Geographic Information Institute). The mission of the satellite is the periodic monitoring of the land for efficient managements. Having completed the calibration and validation processes, CAS500-1 has been acquiring land images to generate the basic geospatial information that are topographic maps, orthorectified images, and DEM (Digital Elevation Model). Using satellite data has a lot of advantages over aerial images in terms of repeatability, coverage, and accessibility. However, the spatial resolution is relatively lower than that of aerial images. This prohibits the large scale topographic mapping from the satellite data. Therefore, we conducted 1:5,000 large scale topographic mappings using CAS500-1 stereo data and assessed the accuracy. Two test areas were selected including accessible and inaccessible areas. Overlapping CAS500-1 data were automatically processed with GCP (Ground Control Points) chips for better positional accuracy. Then they are used for the epipolar resampled images that can be used for stereo-plotting. Through the stereo display quality assessment, we selected the optimal stereo set for each site. The accuracy of the resulted topographic maps was checked using ground truth data such as GNSS (Global Navigation Satellite System)-surveyed points over the accessible area and GCP chips in the inaccessible area, respectively. The experimental results showed the accuracy successfully met the requirement of 1:5,000 topographic map in Korea. We concluded that CAS500-1 data will have more potential for large scale mapping considering that CAS500-2 is scheduled to be launched in 2022 and cooperate with CAS500-1.

Digital elevation models for high-resolution base flood elevation mapping in a densely populated city

Digital elevation models for high-resolution base flood elevation mapping in a densely populated city

ГЕОДЕЗІЯ, КАРТОГРАФІЯ І АЕРОФОТОЗНІМАННЯ

The purpose of the work is to investigate the digital elevation models of the mass grave of 1944, built on the basis of aerial and cartographic data of different times. The implementation of the task involves the construction of digital elevation models (DEM) on the territory of the Jewish mass grave in the city of Vynnyky and Italian prisoners of war near the city. On the territory of the Jewish mass grave in the city of Vynnyky, a DEM was created based on a stereo pair of archival aerial images obtained in 1944 and on the basis of aerial photography from a UAV conducted in 2015. Since archival aerial photographs did not contain orientation elements, they were geometrically transformed using the ErdasImagine software. After that, the stereo pair was processed in the program Digitals, where relief elements on the territory of the mass grave were obtained. Aerial photography from a UAV on the territory of the city of Vynnyky provided the data which was processed in the Agisoft PhotoScan software. It allowed us to create an orthophoto plan and an elevation map of the city territory. Elements of relief on the territory of the mass grave of 1944 and 2015 were imported into the Surfer software environment, where 3D digital elevation models were built. Since the territory of the grave did not have significant differences in height, and the relief was quite gentle, the method of constructing the DEM of the Natural Neighbor was chosen, which gave a positive result. Analyzing the digital elevation model and the vector map for 1944, the border of the mass grave is clearly distinguished, since the height difference between its edges and the rest of the territory is from 20 to 36 cm, depending on the section of the border. The analysis of the digital elevation models and the vector map for 2015 of the territory of the mass grave determined from the aerial image of 1944 showed that the characteristic height differences on the former border of the grave are observed only in the left-right corner of the grave and on a separate section of the right border. Height difference indicators range from 15 to 20 cm. It should be noted that height differences are also observed in the rest of the territory belonging to the mass grave and outside it. This can be explained by many years of human intervention and agricultural land use. With regard to the investigation of the mass graves of Italian prisoners of war near the city of Vynnyky, the modern territory is 100% wooded, which makes it impossible to apply aerial photography. Therefore, in order to compare the modern DEM with the stereo pair of aerial images of 1944, it was decided to conduct a ground tacheometric survey of the area in 2011. Tacheometric survey data was exported to the dxf exchange format, after which it was opened in Surfer and the DEM was built. In this case, digital elevation models were built using the Kriging method, since the area where the mass graves are located is quite hilly with a significant difference in elevation. According to the digital elevation models, 54 pits with burials that have survived to this day have been identified. The scientific novelty of the work consists in the development of the concept of combining disparate data for the construction of digital elevation models and the creation of a complete picture of the study of the territory of the object of historical and cultural heritage. The received data can be used by the relevant cultural heritage preservation departments of the Ministry of Culture of Ukraine for their further study.

Modeling and Prediction of User Stability and Comfortability on Autonomous Wheelchairs With 3-D Mapping

Traditional manual wheelchairs have a fixed seat with no movement or angle adjustment, which can seriously affect the user's comfort and greatly limit user experience. However, the electric wheelchair relies on strong intelligence and automatic features; it can not only realize the multidegree freedom adjustment of the human body and the seat but also has a rich and powerful man–machine control interface, which greatly facilitates and improves the user experience. This study upgraded a Permobil C400-powered wheelchair with multisensor data fusion technology to enrich its terrain recognition, tipping stability, and comfortability prediction. The tipping stability modeling of the wheelchair dummy system is carried out using multibody dynamics and vibration mechanics to obtain the tipping stability limit and the comfort evaluation of the wheelchair vibration acceleration on the human body during travel. Based on the elevation mapping method, the wheelchair can estimate the terrain from the local point of view at any point in time. At the same time, the RGB-D depth camera is connected to the robot operating system (ROS) system, and the open-source algorithm package RTAB-MAP is used to complete the MAP construction and collect the 3-D point-cloud terrain data. Then, the real 3-D terrain files are generated through the point-cloud stitching technology for stability simulation of the wheelchair–human system. The tipping stability and comfort indexes of the wheelchair–human system when passing over different physical terrains can be obtained. The experimental results show that the IMU data located on the human chest agree well with the simulation analysis data and are suitable for a variety of complex real-terrain conditions, verifying the accuracy of the wheelchair–human system dynamics model and the feasibility of the simulation analysis process. Thus, this modeling and simulation method can predict wheelchair stability and user comfortability well and ensure a high-performance experience.

Comparison of anterior segment parameters in normal and keratoconus eyes using combined placido-disk schiempflug imaging-based topography

The aims: The purpose of this study was to evaluate and compare the anterior and posterior corneal surface parameters, keratoconus indices, thickness profile data, and data from enhanced elevation maps of keratoconus and normal corneas using Combined Placido-Schiempflug imaging-based topography, and to determine the sensitivity and specificity of these parameters in discriminating keratoconus from normal eyes.&#x0D; Materials and methods: A retrospective comparative observational study of 100 normal eyes and 100 keratoconus eyes was done from April 2014 to May 2015 at MM Joshi Eye Institute, Hubli, Karnataka. We evaluated and compared the anterior and posterior corneal surface parameters, keratoconus indices, thickness profile data, and data from enhanced elevation maps of keratoconus and normal corneas using Combined Placido-Scheimpflug Imaging based Topography to determine the sensitivity and specificity of these parameters in discriminating keratoconus from normal eyes.&#x0D; Results: Keratoconus indices (Sif, Sib, BCV-f, BCV-b, KVf, KVb, CCT, MinCT) showed Excellent AUROC values followed by K- steep in posterior curvature at 3 mm, 5 mm followed by K- steep in anterior curvature and other parameters in discriminating Normal from Keratoconus-Suspect eyes. Elevation indices- BCV-f, BCV-b, CCT, Min-CT, K-steep &amp; K-flat in anterior and posterior curvature in 3 mm, 5 mm, 7 mm, and CV were significant in discriminating normal from mild keratoconus. All parameters except ACV and ICA were significant in discriminating normal from moderate keratoconus. All parameters were significant in discriminating normal from severe keratoconus eyes. Morphological indices were significant in differentiating mild, moderate and severe keratoconus.&#x0D; Conclusion: Many parameters were statistically significant between keratoconus and normal eyes compared with early keratoconus eyes.&#x0D; The topography and corneal aberration results in this study are promising for detecting ectatic corneas. In our study, thickness indices-CCT, MinCT, Abberometry indices- BCVf, BCVb and keratometry in steep meridian at posterior curvature had the highest AUC scores in differentiating normal from sub-clinical keratoconus.&#x0D; Elevation indices- Rbf-f, Rbf-b, thickness indices- CCT, MinCT, aberrometry indices, and keratometry-steep in anterior &amp; posterior curvature had the highest AUC scores in differentiating moderate and severe keratoconus

Research on the autonomous system of the quadruped robot with a manipulator to realize leader‐following, object recognition, navigation and operation

AbstractA systematic solution is developed to improve the autonomous capability of the quadruped robot with a manipulator, such as navigation, recognition and operation. The developed system adopts novel software, hardware system and system architecture, including a specially designed environment awareness system (EAS). Based on the camera and LiDAR on the EAS, the recognition of multiple common targets, such as the leader, door, window and bag, is achieved. In terms of navigation, a location method is built, that combines the laser odometer and global positioning system. A mapping and path planning module is designed by the Robot‐centric Elevation Mapping algorithm and the rapidly exploring rand tree algorithm. For operation, a real‐time target grasp detection system is proposed based on the You Only Look Once v5 algorithm to improve the success rate of tasks. The whole system is integrated based on the task relevance scheduling strategy to reduce the computational complexity. The tightly integrated system and the subsystems are evaluated by conducting simulations and physical experiments in robot recognition, navigation and operation. Extensive experiments show that the proposed framework can better achieve the autonomous navigation and operation of the quadruped robot with a manipulator. Notably, the proposed framework is still effective when facing dynamic objects. In addition, the system can be easily extended to other forms of mobile robot.

Spatial effects of urban green infrastructure on instream water quality assessed by chemical and sensory indicators

Urban green infrastructure has been simulated effectively and economically to reduce volume and pollutants of stormwater runoffs but its spatial effects remain unclear. A snap sampling campaign was carried out for surface water quality in the downtown waterway network of a pilot sponge city (Suzhou) in China, dividing into 7 subwatersheds according to the digital elevation map. In total, 144 sampling points were investigated and measured for chemical quality of surface water while 68 out of the sampling points had a sensory evaluation questionnaire interview for water quality with 321 respondents, in whom the native residents scored a significant spatiality of water quality. The downtown waterway network had phosphorus-limited eutrophic surface water with total nitrogen worse than Class V of the national guidelines. Chemical and sensory evaluation indexes of surface water quality had significant spatial consistency (p < 0.001). All types of green spaces (%) in subwatershed, especially along the urban waterway network (waterfront) and roadside, and in the 100 m riparian buffer zone, significantly influenced nutrient loads in surface water. Findings of the present study suggest that the 100 m riparian buffer zone would be priority areas and the waterfront and roadside should be the highly efficient spots for planning strategy on urban green infrastructure implementation to reduce nutrient loads in surface water and to improve urban landscape aesthetics.

A Resilient Forward-Looking Terrain Avoidance Warning Method for Helicopters

To reduce nuisance alarms caused by a fixed forward-looking boundary, this paper proposes a resilient forward-looking terrain avoidance method. The method constructed the resilient and adjustable forward-looking boundary model by using roll angle and navigation error, which makes the terrain data employed by the forward-looking alert closer to the terrain data overflown by the anticipated flight trajectory, reduces the nuisance alarm, and improves the reliability of the alert. Using real digital elevation maps and simulated flight testing, the proposed method was observed to provide alerting that is superior to the traditional forward-looking terrain avoidance technique.

After the Revolution: A Review of 3D Modelling as a Tool for Stone Artefact Analysis

With over 200 peer-reviewed papers published over the last 20 years, 3D modelling is no longer a gimmick but an established and increasingly common analytical tool for stone artefact analysis. Laser and structured light scanning, photogrammetry, and CT scanning have all been used to model stone artefacts. These have been combined with a variety of different analytical approaches, from geometric morphometrics to custom reduction indices to digital elevation maps. 3D lithic analyses are increasingly global in scope and studies aim to address an ever-broadening breadth of research topics ranging from testing the functional efficiency of artefacts to assessing the cognitive capabilities of hominid populations. While the impact of the computational revolution on lithic analysis has been reviewed, the impact of 3D modelling on lithic analysis has yet to be comprehensively assessed. This paper presents a review of how 3D modelling in particular has impacted the field of stone artefact analysis. It combines a quantitative bibliometric analysis with a qualitative review to assess just how “revolutionary” 3D modelling has been for lithic analysis. It explores trends in the use of 3D modelling in stone artefact analysis, its impact on the wider lithic analysis field, and methodological, regional and theoretical gaps which future research projects could explore.

Determining Effective Environmental Factors in the Distribution of Endangered Endemic Medicinal Plant Species Using the BMLR Model: The Example of Wild Celery (Kelussia odoratissima Mozaff., Apiaceae) in Zagros (Iran).

Kelussia&amp;nbsp;odoratissima Mozaff. is a medicinal species native to Iran. The goal of this research was to determine the environmental factors important for the distribution of K. doratissima in Iran using BMLR modeling. Six random transects were established throughout the species' habitat, and 220 quadrats with an area of 4 m2 were plotted. The canopy cover percentages of K. doratissima were estimated in each quadrat. Topographic factors, including elevation, slope, and aspect maps, were generated by creating DEM images. Land use, land evaluation, evaporation, temperature, and precipitation maps of the area were created accordingly. The data collected from the experiments were analyzed using the Minitab and R statistical packages. To determine the effect of the studied factors in the distribution of K. doratissima, we ran a set of backward multiple linear regressions. The results showed that the effects of evaporation, elevation, and slope were significant in the species' distribution, with elevation having a positive effect and evaporation and slope showing negative effects. Further, elevation had the highest effect on distribution (greatest absolute value of beta at 9.660). The next most significant factors in the plant's distribution were evaporation (beta = 8.282) and slope (beta = 0.807), respectively.

Mapping the Groundwater Potential Areas by Using the GIS Method in Kg Samawang, Sandakan, Sabah, Malaysia.

This study aims to study the groundwater potential areas around Kg Samawang, Sandakan by using the Geographic Information System (GIS) method. There are few basic maps and data were used in this study, which is geology, drainage, soil type, lineament, topography, satellite images and rainfall distribution data. A total of seven thematic maps were produced including the lineament density map, drainage density map, landuse map, soil type map, rainfall distribution map, lithology map and topographic elevation map by using the ILWIS 3.0 software. All the thematic maps will be given a weightage based on its compensation to groundwater availability before being integrated based on the Hopkins Linear Equation using the Multi-Class Index Overlay model. This process will produce a forecast map of the groundwater potential at the study area. As a result of the forecast map, it is estimated that 40% of the study area has high potential of groundwater while 60% has medium, low and very low potential. It is hoped that this groundwater potential map can be used for the exploration work in the future.

New Perspectives of Earth Surface Remote Detection for Hydro-Geomorphological Monitoring of Rivers

In the current scenery of climate change and its relatively increasing visible effects seen over the world, the monitoring of geomorphological processes and flood dynamics becomes more and more necessary for disaster risk reduction. During recent decades, the advantages offered by remote sensing for Earth surface observations have been widely exploited, producing images, digital elevation models (DEM), maps, and other tools useful for hydro-geomorphological parameters detection, flood extent monitoring, and forecasting. However, today, advanced technologies and integrated methodologies do not yet enable one to completely provide near-real-time (NRT) and very-high-resolution (VHR) observations of a river, which is needed for risk evaluation and correct operational strategy identification. This work presents an advanced remote detection analysis system (ARDAS) based on the combination of multiple technologies, such as Unmanned Aerial Vehicle (UAV) systems, Structure from Motion (SfM) techniques, and cloud computing environment. The system allows to obtain VHR products, such as ortho-photomosaics and DEM, for deep observation of the river conditions, morphological modifications, and evolution trend. The test of ARDAS in the Basento river catchment area (Basilicata, South Italy) showed that the innovative system (i) proves to be advantageous in river monitoring due to its high accuracy, quickness, and data flexibility; (ii) could represent a NRT solution for timely support of flood hazard assessments; and (iii) can be further developed by integrating other technologies for direct application in land planning and safeguard activities by contributing to the value chain of the new space economy and sustainable development.

Landslide hazard and susceptibility maps derived from satellite and remote sensing data using limit equilibrium analysis and machine learning model

Landslide susceptibility mapping and landslide hazard mapping are approaches used to assess the potential for landslides and predict the occurrence of landslides, respectively. We evaluated and tested a limit equilibrium approach to produce a local-scale, multi-temporal geographic information system-based landslide hazard map that utilized satellite soil moisture data, soil strength and hydrologic data, and a high-resolution (1.5 m) LiDAR-derived digital elevation map. The final multi-temporal landslide hazard map was validated temporally and spatially using four study sites at known landslide locations and failure dates. The resulting product correctly indicated low factor of safety values at the study sites on the dates the landslide occurred. Also, we produced a regional-scale landslide susceptibility map using a logistic regression machine learning model using 15 variables derived from the geomorphology, soil properties, and land-cover data. The area under the curve of the receiver operating characteristic curve was used for the accuracy of the model, which yielded a success rate of 0.84. We show that using publicly available data, a multi-temporal landslide hazard map can be created that will produce a close-to-real-time landslide predictive map. The landslide hazard map provides an understanding into the evolution of landslide development temporally and spatially, whereas the landslide susceptibility map indicates the probability of landslides occurring at specific locations. When used in tandem, the two mapping models are complementary to each other. Specifically, the landslide susceptibility mapping identifies the area most susceptible to landslides, while the landslide hazard mapping predicts when landslide may occur within the identified susceptible area.

Design and Implementation of LIDAR System for Distance Measurements

LIDAR (Light Detection and Ranging) is an optical remote sensing system that can measure the distance to a target by shining light on the target. LIDAR technology is used in robotics to recognize the environment and classify objects. The ability of LIDAR technology to provide a 2D elevation map of the terrain, a highly accurate distance to the ground, and an approach speed allows robots and manned vehicles to land accurately and safely. LIDAR consists of a transmitter that illuminates the target with a laser beam and a receiver that can detect light components that are substantially coaxial with the transmitted beam. The receiver sensor calculates the distance based on the time it takes for the light to reach and return to its destination. A mechanical mechanism with a mirror uses a rotating nodding mirror to pan the light beam and cover the desired scene in one plane or even in 3D. LIDAR can measure distances up to 40 meters, making this system suitable for urban environments. The rider was mounted on a stepper motor and covered the entire area. This paper describes the design and implementation of LIDAR for distance measurement. Keywords: Arduino Nano, LIDAR, VL53L0X sensor, Servomotor, Processing Software.

Global path planning algorithm based on double DQN for multi-tasks amphibious unmanned surface vehicle

It is a key to making path planning for an amphibious unmanned surface vehicle (USV). A global path planning algorithm based on double deep Q networks (DDQN) is proposed. Firstly, an environment model is constructed by an electronic nautical chart and elevation map to train and verify the algorithm. Secondly, based on the kinematics of amphibious USV, a Markov decision process (MDP) framework is built, and various reward functions are designed for diverse tasks. During the training, obstacles and water depth information of the environment are used, the amphibious USV agent is guided to the target area. Meanwhile, based on the prior knowledge, an action mask approach is integrated to deal with the invalid actions generated by the amphibious USV. Path smoothing is also integrated to smooth the path. According to different criteria, reasonable paths can be generated and adjusted by the weights of the reward function. To verify our algorithm, a small-scale simulation environment is established, and two scenarios are introduced. The results show that our DDQN algorithm can generate reasonable global paths for diverse tasks. Moreover, compared with DQN, A*, and RRT algorithms, the paths generated by our method have better performance.

Angle κ and its effect on the corneal elevation maps in refractive surgery candidates.

To assess the associations of angle κ and Pentacam decentration indices with elevation maps in normal refractive surgery candidates. Salouti Cornea Research Center, Salouti Eye Clinic, Shiraz, Iran. Retrospective observational study. In this research, the right eyes of 173 refractive surgery candidates were assessed. Data of front and back corneal elevation maps, keratometric data, decentration indices, and corneal astigmatism obtained by Pentacam HR system and angle κ obtained by Orbscan IIz were extracted. Maximum elevation (or depression) for each of the 4 quadrants was recorded. Correlations of elevation values with angle κ, Pentacam decentration indices, keratometry, and astigmatism were examined by Pearson correlation coefficient. 148 age- and sex-matched cases with keratoconus grade 1 were selected as a positive control group, and Pentacam variables were compared between the groups. Overall, data from 173 eyes of 173 normal refractive surgery candidates and 148 eyes of 148 patients with keratoconus were recorded and analyzed. In normal refractive surgery candidates, the mean of angle κ was 5.32 ± 1.36 (SD) degrees. Angle κ had a positive correlation with front and back temporal elevations based on a best-fit sphere (BFS) ( r = 0.339, P = .001; r = 0.300, P < .001, respectively). Front and back keratometric astigmatisms were positively correlated with front and back nasal and temporal elevations ( r ≥ 0.543, P < .001) and negatively correlated with superior and inferior elevations ( r ≤ -0.547, P < .001). These associations no longer existed when using best-fit toric ellipse (BFTE) for calculating elevation data. The thinnest point to vertex decentration was significantly associated with the back temporal elevation (based on the BFTE) in both normal ( r = 0.311, P < .001) and keratoconus ( r = 0.190, P = .021) eyes. This study showed that elevation maps obtained by the Pentacam system using BFS might be affected by both the large angle κ and corneal astigmatism, confounding the preoperative assessment of refractive surgery candidates. Using BFTE as a reference for calculating elevation data should eliminate most diagnostic issues and thus is encouraged in this context.