Mapping Errors Research Articles

Open source map matching with Markov decision processes: A new method and a detailed benchmark with existing approaches

AbstractMap matching is a widely used technology for mapping tracks to road networks. Typically, tracks are recorded using publicly available Global Navigation Satellite Systems, and road networks are derived from the publicly available OpenStreetMap project. The challenge lies in resolving the discrepancies between the spatial location of the tracks and the underlying road network of the map. Map matching is a combination of defined models, algorithms, and metrics for resolving these differences that result from measurement and map errors. The goal is to find routes within the road network that best represent the given tracks. These matches allow further analysis since they are freed from the noise of the original track, they accurately overlap with the road network, and they are corrected for impossible detours and gaps that were present in the original track. Given the ongoing need for map matching in mobility research, in this work, we present a novel map matching method based on Markov decision processes with Reinforcement Learning algorithms. We introduce the new Candidate Adoption feature, which allows our model to dynamically resolve outliers and noise clusters. We also incorporate an improved Trajectory Simplification preprocessing algorithm for further improving our performance. In addition, we introduce a new map matching metric that evaluates direction changes in the routes, which effectively reduces detours and round trips in the results. We provide our map matching implementation as Open Source Software (OSS) and compare our new approach with multiple existing OSS solutions on several public data sets. Our novel method is more robust to noise and outliers than existing methods and it outperforms them in terms of accuracy and computational speed.

An adaptive fringe projection method for 3D measurement with high-reflective surfaces

Fringe projection profilometry (FPP) has the advantages of non-contact, quick and high accuracy, which is popular in the fields of industrial inspection and measurement. However, the coded fringe patterns may be failure suffered from the high reflectivity of the measured surfaces. Aiming at this problem, an adaptive fringe projection method is proposed in this paper by the best projection intensity strategy and the coordinate mapping establishment between camera and projection regions. Combining the local camera response function, the dichotomizing search is used to confirm the best projection intensity of overexposed regions by pixel-wise. The unwrapping phase map of background is performed to establish the coordinate mapping relationship of measured overexposed regions between the camera and projection images, which is robust and convenient. The mapping errors caused by inaccurate phase information in the overexposed regions are effectively avoided, and the fringe projection patterns are adjusted adaptively, without additional equipment and low-expose patterns. Both theory and experiments have verified the feasibility and superiority of the proposed method.

Saliency as Pseudo-Pixel Supervision for Weakly and Semi-Supervised Semantic Segmentation.

Existing studies on semantic segmentation using image-level weak supervision have several limitations, including sparse object coverage, inaccurate object boundaries, and co-occurring pixels from non-target objects. To overcome these challenges, we propose a novel framework, an improved version of Explicit Pseudo-pixel Supervision (EPS++), which learns from pixel-level feedback by combining two types of weak supervision. Specifically, the image-level label provides the object identity via the localization map, and the saliency map from an off-the-shelf saliency detection model offers rich object boundaries. We devise a joint training strategy to fully utilize the complementary relationship between disparate information. Notably, we suggest an Inconsistent Region Drop (IRD) strategy, which effectively handles errors in saliency maps using fewer hyper-parameters than EPS. Our method can obtain accurate object boundaries and discard co-occurring pixels, significantly improving the quality of pseudo-masks. Experimental results show that EPS++ effectively resolves the key challenges of semantic segmentation using weak supervision, resulting in new state-of-the-art performances on three benchmark datasets in a weakly supervised semantic segmentation setting. Furthermore, we show that the proposed method can be extended to solve the semi-supervised semantic segmentation problem using image-level weak supervision. Surprisingly, the proposed model also achieves new state-of-the-art performances on two popular benchmark datasets.

A Mapping Methodology Adapted to all Polar and Subpolar Oceans with a Stretching/Shrinking Constraint

Abstract Polar and subpolar oceans play a particularly important role in the global climate and its temporal changes, yet these regions are less well sampled than the rest of the global ocean. To better understand the physical or biogeochemical properties and their variabilities in these regions, accurate data mapping is crucial. In this paper, we introduce a mapping methodology that includes a water column shrinking and stretching constraint (SSC) based on the principle of conservation of potential vorticity. To demonstrate the mapping scheme efficiency, we map the ocean temperature in the southern Sea of Okhotsk, where the bottom topography comprises a broad and shallow shelf, a sharp continental slope, and a deep oceanic basin. Such topographic features are typical of polar and subpolar marginal seas. Results reveal that the SSC integrated (SSCI) mapping strongly reduces the mapping error in the broad and shallow shelf compared with a recently introduced topographic constraint integrated (TCI) mapping procedure. We also tested our mapping scheme in the Southern Ocean, which has a comparatively slanted shelf, a wider and gentler slope, and a deep and broad oceanic basin. We found that the SSCI and TCI methods are practically equivalent there. The SSCI mapping is thus an effective method to map the ocean’s properties in various topographic environments and should be adequate in all polar and subpolar regions. Importantly, we introduced a standardized procedure for determining the decorrelation length scales—a necessary step prior to implementing any mapping scheme—in any topographic conditions.

Distilled representation using patch-based local-to-global similarity strategy for visual place recognition

Visual Place Recognition (VPR) is important for ensuring the accuracy and reliability of re-localization in a Visual Simultaneous Localization and Mapping (VSLAM) system, effectively reducing potential errors in mapping and navigation tasks. In VPR tasks, CNN-based VPR techniques encounter challenges in mitigating the impact of severe appearance changes caused by seasons and weather, as well as, viewpoint changes arising from robot motion deviations. To cope with this problem, a local-to-global similarity strategy is proposed in this paper. Specifically, an Auto-Encoder (AE) block is designed to distill appearance-invariant local features from AlexNet, where each local feature represents a specific image patch. Then, three local similarity measures, namely paired similarity, additional similarity, and adjacent similarity, are used to measure the similarity between paired images. Finally, weight encoders are introduced to combine the three local measures into a global one that achieves viewpoint-invariance. Extensive experiments show that our proposed method is robust to severe appearance and viewpoint changes while outperforming the current state-of-the-art methods on public visual place recognition datasets. Moreover, the proposed similarity strategy distinguishes the relationships between internal and external patches within images, effectively enhancing its recognition capability in real-world scenarios.

A Simulation of Calibration and Map-making Errors of the Tianlai Cylinder Pathfinder Array

The Tianlai cylinder array is a pathfinder for developing and testing 21 cm intensity mapping techniques. In this paper, we use numerical simulation to assess how its measurement is affected by thermal noise and the errors in calibration and map-making process, and the error in the sky map reconstructed from a drift scan survey. Here we consider only the single frequency, unpolarized case. The beam is modeled by fitting to the electromagnetic simulation of the antenna, and the variations of the complex gains of the array elements are modeled by Gaussian processes. Mock visibility data are generated and run through our data processing pipeline. We find that the accuracy of the current calibration is limited primarily by the absolute calibration, where the error comes mainly from the approximation of a single dominating point source. We then studied the m-mode map-making with the help of Moore–Penrose inverse. We find that discarding modes with singular values smaller than a threshold could generate visible artifacts in the map. The impacts of the residue variation of the complex gain and thermal noise are also investigated. The thermal noise in the map varies with latitude, being minimum at the latitude passing through the zenith of the telescope. The angular power spectrum of the reconstructed map show that the current Tianlai cylinder pathfinder, which has a shorter maximum baseline length in the North–South direction, can measure modes up to l ≲ 2π b NS/λ ∼ 200 very well, but would lose a significant fraction of higher angular modes when noise is present. These results help us to identify the main limiting factors in our current array configuration and data analysis procedure, and suggest that the performance can be improved by reconfiguration of the array feed positions.

Finding Misclassified Natura 2000 Habitats by Applying Outlier Detection to Sentinel-1 and Sentinel-2 Data

The monitoring of Natura 2000 habitats (Habitat Directive 92/43/EEC) is a key activity ensuring the sufficient protection of European biodiversity. Reporting on the status of Natura 2000 habitats is required every 6 years. Although field mapping is still an indispensable source of data on the status of Natura 2000 habitats, and very good field-based data exist in some countries, keeping the field-based habitat maps up to date can be an issue. Remote sensing techniques represent an excellent alternative. Here, we present a new method for detecting habitats that were likely misclassified during the field mapping or that have changed since then. The method identifies the possible habitat mapping errors as the so-called “attribute outliers”, i.e., outlying observations in the feature space of all relevant (spectral and other) characteristics of an individual habitat patch. We used the Czech Natura 2000 Habitat Layer as field-based habitat data. To prepare the feature space of habitat characteristics, we used a fusion of Sentinel-1 and Sentinel-2 satellite data along with a Digital Elevation Model. We compared outlier ratings using the robust Mahalanobis distance and Local Outlier Factor using three different thresholds (Tukey rule, histogram-based Scott’s rule, and 95% quantiles in χ2 distribution). The Mahalanobis distance thresholded by the 95% χ2 quantile achieved the best results, and, because of its high specificity, appeared as a promising tool for identifying erroneously mapped or changed habitats. The presented method can, therefore, be used as a guide to target field updates of Natura 2000 habitat maps or for other habitat/land cover mapping activities where the detection of misclassifications or changes is needed.

Widespread Errors in Bus Stop Location Mapping is an Accessibility Barrier for Passengers Who are Blind or Have Low Vision

Widespread Errors in Bus Stop Location Mapping is an Accessibility Barrier for Passengers Who are Blind or Have Low Vision

Long-term life stress mapping algorithm of the deep-sea pressurized spherical shell based on digital-twin technology

Aiming at the difficulty of the global stress field monitoring of the deep-sea pressurized spherical shell in real time, this paper proposes a long-term life stress field reverse mapping algorithm based on digital-twin technology. Specifically, a high-fidelity FEM model is established based on the titanium alloy data, structure construction data and model test data. The Elastic modulus E and Poisson's ratio υ are selected as the structure characteristic parameters based on the preliminary small sample test results. The sensitivity analysis is carried out. And then, stress monitoring location selection results is proposed considering the factors such as mutual independence, installation conditions, and economic costs. The Long-Short Term Memory network (LSTM) together with the FEM results is tailored and employed to build the reverse mapping model. The mapping error is below 1% and compared to the FEM model, the efficiency of the proposed model is improved by more than 60 times.

Marketing insight from consumer reviews: Creating brand position through opinion mining approach

PurposeBrand managers can determine the ideas, perceptions, or experiences of consumers about their brands and their competitors through traditional marketing research techniques such as focus group discussions, surveys, and in-depth interviews. Through these methods, brands can observe the visual expression of consumer perceptions concretely by using perception maps or radar chart techniques. However, traditional methods have certain limitations. The validity of brand positioning in perception maps may be questioned since consumers' answers to survey questions can be misleading, consumers may express opinions about a product that they have not purchased yet, and other methods may divert the opinions of consumers at a specific stage.In this study, global brand positions in the minds of consumers were concretized through perception maps by analyzing product comments on e-commerce sites through the use of opinion mining/sentiment analysis, one of the text mining methods. Moreover, perception maps and radar charts were created for the selected brands by forming the dataset from consumers’ reviews about brands and products in online environments. perception map errors caused by traditional methods have been prevented by using opinion mining/sentiment analysis, which constitutes the originality of the research. Design/methodology/approachIn this study, the text mining method, which includes opinion mining and sentiment analysis techniques, has been used to create perceptual maps from consumer reviews about products on e-commerce platforms. The TF*IDF method is used to identify the features to be compared in the perceptual map, which selects the features with weighted importance among the features mentioned by consumers in their comments. In this study, a lexicon-based opinion mining technique has been used to assign sentiment scores to consumers' comments on e-commerce websites for each product and its specified features. FindingsThe findings of the study have illustrated the areas in which the brands are evaluated as “insufficient” or “successful” by consumers within the framework of the characteristics selected in the study. It has been revealed that the method used in the research can provide accurate results for brand managers to make positioning decisions and develop effective marketing strategies. Research limitations/implicationsThe research has been conducted by analyzing product reviews about selected brands on e-commerce platforms. The comments selected within the scope of the research include only those written in the Turkish language, and the evaluations have been carried out on the comments made until a specific date. Originality/valueIn this study, the analyses of the comments on the selected brands have ensured that the businesses are informed of the level of perception about their products in the market and that the research is based on a holistic approach that helps other consumers to develop a perspective on a specific product based on the comments of a consumer who shares his/her experience about the aforesaid product.

Cost-efficient bathymetric mapping method based on massive active–passive remote sensing data

Accurate bathymetric mapping is critical for island reef research, coastal ecosystem monitoring, and nearshore engineering. Increasing amounts of remote sensing data have promoted the development of optical bathymetric remote sensing. However, large-scale and efficient bathymetric mapping of shallow oceanic islands is difficult due to the limited availability of high-quality optical remote sensing images. In this study, we propose a cost-efficient method for bathymetric mapping based on a quadratic polynomial ratio model (QPRM) of massive active–passive remote sensing data. Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) data represent active data and a priori data, and Sentinel-2 data from the Google Earth Engine represent passive data. The key step is to build a QPRM based on the median value of the blue-green band logarithmic ratio from massive Sentinel-2 data. The method is applied to six typical areas located in the Pacific Ocean, Indian Ocean, and South China Sea, and the results showed that the root mean square error (RMSE) and mean absolute error (MAE) of bathymetric mapping in the water depth range of 0–20 m were 0.49–0.71 m and 0.29–0.49 m, respectively. In addition, the RMSE decreased with an increase in the number of Sentinel-2 images, and the results were relatively stable when the number reached approximately 150. The QPRM results were also compared with that of the classical linear ratio model (CLRM) and multiple fitting methods to calculate the median water depth, and the findings showed that the QPRM method has a slight advantage in terms of accuracy and efficiency. The proposed method, which does not depend on in situ data, effectively calculates precise water depths and removes the effects of complex underwater optical paths and surface instabilities, such as waves, boats, and clouds. Therefore, it has great potential for bathymetric mapping of oceanic islands and reefs worldwide.

Nonlinear back-end optimization method for VSLAM with multi-convex combined maximum correntropy criterion

Back-end optimization plays a key role in eliminating the accumulated error in Visual Simultaneous Localization And Mapping (VSLAM). Existing back-end optimization methods are usually premised on the Gaussian noise assumption which does not always hold true due to the non-convex nature of the image and the fact that non-Gaussian noises are often encountered in real scenes. In view of this, we propose a back-end optimization method based on Multi-Convex combined Maximum Correntropy Criterion (MCMCC). A MCMCC-based cost function is first tailored for nonlinear back-end optimization in the context of VSLAM and the optimization problem is solved through Levenberg–Marquardt algorithm iteratively. Then, the proposed method is applied to ORB-SLAM3 to test its performance on public indoor and outdoor datasets. The real time performance is also validated using a RaceBot platform in real indoor and outdoor environments. In addition, the reprojection error is statistically analyzed to demonstrate the non-Gaussian characteristics in the back-end optimization process. Finally, the suggestion parameters are also provided through experiments for further study.

Improving the accuracy of computational fluid dynamics simulations of coiled cerebral aneurysms using finite element modeling

Cerebral aneurysms are a serious clinical challenge, with ∼half resulting in death or disability. Treatment via endovascular coiling significantly reduces the chances of rupture, but the techniquehas failure rates of ∼20 %. This presents a pressing need to develop a method fordetermining optimal coildeploymentstrategies. Quantification of the hemodynamics of coiled aneurysms using computational fluid dynamics (CFD) has the potential to predict post-treatment outcomes, but representing the coil mass in CFD simulations remains a challenge. We use the Finite Element Method (FEM) for simulating patient-specific coil deployment for n = 4 ICA aneurysms for which 3D printed in vitro models were also generated, coiled, and scanned using ultra-high resolution synchrotron micro-CT. The physical and virtual coil geometries were voxelized onto a binary structured grid and porosity maps were generated for geometric comparison. The average binary accuracy score is 0.8623 and the average error in porosity map is 4.94 %. We then conduct patient-specific CFD simulations of the aneurysm hemodynamics using virtual coils geometries, micro-CT generated oil geometries, and using the porous medium method to represent the coil mass. Hemodynamic parameters including Neck Inflow Rate (Qneck) and Wall Shear Stress (WSS) were calculated for each of the CFD simulations. The average relative error in Qneck and WSS from CFD using FEM geometry were 6.6 % and 21.8 % respectively, while the error from CFD using a porous media approximation resulted in errors of 55.1 % and 36.3 % respectively; demonstrating a marked improvement in the accuracy of CFD simulations using FEM generated coil geometries.

NgsJulia: population genetic analysis of next-generation DNA sequencing data with Julia language

A sound analysis of DNA sequencing data is important to extract meaningful information and infer quantities of interest. Sequencing and mapping errors coupled with low and variable coverage hamper the identification of genotypes and variants and the estimation of population genetic parameters. Methods and implementations to estimate population genetic parameters from sequencing data available nowadays either are suitable for the analysis of genomes from model organisms only, require moderate sequencing coverage, or are not easily adaptable to specific applications. To address these issues, we introduce ngsJulia, a collection of templates and functions in Julia language to process short-read sequencing data for population genetic analysis. We further describe two implementations, ngsPool and ngsPloidy, for the analysis of pooled sequencing data and polyploid genomes, respectively. Through simulations, we illustrate the performance of estimating various population genetic parameters using these implementations, using both established and novel statistical methods. These results inform on optimal experimental design and demonstrate the applicability of methods in ngsJulia to estimate parameters of interest even from low coverage sequencing data. ngsJulia provide users with a flexible and efficient framework for ad hoc analysis of sequencing data.ngsJulia is available from: https://github.com/mfumagalli/ngsJulia.

A low-coverage 3' RNA-seq to detect homeolog expression in polyploid wheat.

Although allopolyploid species are common among natural and crop species, it is not easy to distinguish duplicated genes, known as homeologs, during their genomic analysis. Yet, cost-efficient RNA sequencing (RNA-seq) is to be developed for large-scale transcriptomic studies such as time-series analysis and genome-wide association studies in allopolyploids. In this study, we employed a 3' RNA-seq utilizing 3' untranslated regions (UTRs) containing frequent mutations among homeologous genes, compared to coding sequence. Among the 3' RNA-seq protocols, we examined a low-cost method Lasy-Seq using an allohexaploid bread wheat, Triticum aestivum. HISAT2 showed the best performance for 3' RNA-seq with the least mapping errors and quick computational time. The number of detected homeologs was further improved by extending 1 kb of the 3' UTR annotation. Differentially expressed genes in response to mild cold treatment detected by the 3' RNA-seq were verified with high-coverage conventional RNA-seq, although the latter detected more differentially expressed genes. Finally, downsampling showed that even a 2 million sequencing depth can still detect more than half of expressed homeologs identifiable by the conventional 32 million reads. These data demonstrate that this low-cost 3' RNA-seq facilitates large-scale transcriptomic studies of allohexaploid wheat and indicate the potential application to other allopolyploid species.

Eliminating Mapping Error of Link Quality Prediction for Low-Power Wireless Networks

The size of time windows used by low power wireless protocols to compute packet reception ratio (PRR) directly affects the accuracy and agility of link quality prediction. Using small time windows would make the prediction more agile, but its accuracy decreases seriously. To improve the accuracy while maintaining agility, many existing methods generally predict the physical layer parameters within small time windows first and then calculate PRR using mapping models of such parameters and PRR. However, due to the propagation characteristics of wireless signal, these mapping models usually introduce large errors. The mapping error will superimpose on the prediction error of physical layer parameters, which would inevitably degrade the accuracy and reliability of PRR prediction. This paper proposes to eliminate the step of mapping from physical layer parameters to PRR and directly use the historical series of PRR computed within small time windows as input parameters to build prediction model. Then, the PRR within large time windows could be predicted. This effectively avoids the errors introduced by using mapping models while not sacrificing the agility. To verify the proposed method, several machine learning algorithms were chosen to implement the prediction model. Compared with similar methods based on mapping models, the proposed one could achieve higher accuracy under different size of time windows. Specifically, under large time windows which are commonly used to describe PRR in practice, its accuracy is much higher. More importantly, agility of the proposed method is basically equivalent or even superior to existing ones.

Language in the Hypnotherapy of Depression Healing: A Neurolinguistic Study

The neurolinguistic concept is similar to language function in that it expresses thought that leads to behavior, habit, and character. This study focuses on neurolinguistics to investigate the language used in hypnotherapy. Language is a therapy medium for curing depressive disorders resulting from different factors. Most of the depressive disorders treated at the Bali Brahma Kuntha Center Clinic are caused by disharmony or connection interruption between the left and right hemispheres of the brain, resulting in perception mapping errors. This mind disharmony is responsible for depressive disorder. This study employs a descriptive qualitative approach with a corpus of language or text data to cure depressive disorders. The corpus is the text used by the participants involved in the curing activities between the patients and the therapist. The data was collected using the observation method via recording and note-taking. The results reveal that language is closely related and cannot be separated from a depressive disorder. The type of hypnotherapy used to cure depressive disorder employs religious doctrines, referred to as theohypnotherapy. Overall, language or text is completed with the body, with hand touch induction especially used in the curing procedures. The hypnotherapy results indicate all the patients, based on the testimony texts, recovered from the depressive disorder.

A dynamic large-deformation particle finite element method for geotechnical applications based on Abaqus

In this paper, the application of Abaqus-based particle finite element method (PFEM) is extended from static to dynamic large deformation. The PFEM is based on periodic mesh regeneration with Delaunay triangulation to avoid mesh distortion. Additional mesh smoothing and boundary node smoothing techniques are incorporated to improve the mesh quality and solution accuracy. The field variables are mapped from the old to the new mesh using the closest point projection method to minimize the mapping error. The procedures of the proposed Abaqus-based dynamic PFEM (Abaqus-DPFEM) analysis and its implementation in Abaqus are detailed. The accuracy and robustness of the proposed approach are examined via four illustrative numerical examples. The numerical results show a satisfactory agreement with published results and further confirm the applicability of the Abaqus-DPFEM to solving dynamic large-deformation problems in geotechnical engineering.

Image Mapping Accuracy Evaluation Using UAV with Standalone, Differential (RTK), and PPP GNSS Positioning Techniques in an Abandoned Mine Site.

Global navigation satellite systems (GNSSs) provide a common positioning method that utilizes satellite signals to determine the spatial location of a receiver. However, there are several error factors in standalone GNSS positioning due to instrumental, procedural, and environmental factors that arise during the signal transmission process, and the final positioning error can be up to several meters or greater in length. Thus, real-time kinematic (RTK) correction and post-mission precise point positioning (PPP) processing technologies are proposed to improve accuracy and accomplish precise position measurements. To evaluate the geolocation accuracy of mosaicked UAV images of an abandoned mine site, we compared each orthomosaic image and digital elevation model obtained using standalone GNSS positioning, differential (RTK) GNSS positioning, and post-mission PPP processing techniques. In the three types of error evaluation measure (i.e., relative camera location error, ground control points-based absolute image mapping error, and volumetric difference of mine tailings), we found that the RTK GNSS positioning method obtained the best performance in terms of the relative camera location error and the absolute image mapping error evaluations, and the PPP post-processing correction effectively reduced the error (69.5% of the average total relative camera location error and 59.3% of the average total absolute image mapping error) relative to the standalone GNSS positioning method. Although differential (RTK) GNSS positioning is widely used in positioning applications that require very high accuracy, post-mission PPP processing can also be used in various fields in which it is either not feasible to operate expensive equipment to receive RTK GNSS signals or network RTK services are unavailable.

Improved environmental mapping and validation using bagging models with spatially clustered data

Spatially clustered sampling may result in non-independent data that pose challenges for environmental mapping applications. Two outstanding challenges resulting from the use of spatially clustered data for predictive geospatial modelling with machine learning approaches are biased model training and validation. These issues can be severe for popular bagging models such as Random Forest, yet one or both are often ignored or are handled using sub-optimal approaches. We propose to address these challenges using information on both the spatial autocorrelation of map errors and the spatial sampling intensity. This is achieved by applying the residual spatial covariance as a weighting function for the bagging procedure and for the calculation of weighted validation statistics. Using this approach, the full feature space of the sample data is retained during model training and validation. The utility of covariance weighting for these purposes is investigated through extensive simulation with a range of sample clustering configurations. Results are benchmarked against existing approaches. Covariance weighting improved model performance across a range of clustering scenarios but appeared to produce the greatest improvements for highly clustered data. Covariance-weighted validation demonstrated low bias across a broad range of clustering scenarios compared to existing spatial methods. Findings also suggest, though, that conditional Gaussian simulation approaches may perform well when the proportion of clustered data is very high. Covariance weighting is straightforward to implement, computationally efficient, and scales to different sample sizes and spatial extents.