Traditional Mapping Research Articles

A method of face texture fusion based on visibility weight

In the reconstruction process from 2D images to 3D face models, texture completion still suffers from pixel blurring and color inconsistency when face images are under different perspectives. In this paper, we propose a method based on visibility weights for face texture fusion. Meanwhile, for the complex geometric structure of the ear region where the traditional texture mapping algorithm is inapplicable, a skin color probability method with Gaussian model is used for pixel completion, and jointly optimized with the texture fusion band. Finally, we generate a complete and high-fidelity face texture model. The simulation experiment shows that the novel face texture fusion and completion method generates the perfect texture under multiple viewpoints. Our face texture model outperforms state-of-the-art techniques under the same rendering conditions.

GeoInfographic design of the multi-spatiality of borders: A case study of epidemic prevention and control in Ruili, China.

We live in a world of borders, which influence our perception and movement. Traditional mapping techniques show limitations as borders have become shifting and complex, and borders' multi-scale and multi-spatial properties have been strengthened significantly. To fill the knowledge gap, we explored the multi-spatiality of borders and provided approaches for border symbol design and visualization by taking the coronavirus-hit border city, Ruili, China, as an example. This work could shed light on multi-spatial geographic visualization and policy-making.

Reducing the Dimensionality of SPD Matrices with Neural Networks in BCI

In brain–computer interface (BCI)-based motor imagery, the symmetric positive definite (SPD) covariance matrices of electroencephalogram (EEG) signals with discriminative information features lie on a Riemannian manifold, which is currently attracting increasing attention. Under a Riemannian manifold perspective, we propose a non-linear dimensionality reduction algorithm based on neural networks to construct a more discriminative low-dimensional SPD manifold. To this end, we design a novel non-linear shrinkage layer to modify the extreme eigenvalues of the SPD matrix properly, then combine the traditional bilinear mapping to non-linearly reduce the dimensionality of SPD matrices from manifold to manifold. Further, we build the SPD manifold network on a Siamese architecture which can learn the similarity metric from the data. Subsequently, the effective signal classification method named minimum distance to Riemannian mean (MDRM) can be implemented directly on the low-dimensional manifold. Finally, a regularization layer is proposed to perform subject-to-subject transfer by exploiting the geometric relationships of multi-subject. Numerical experiments for synthetic data and EEG signal datasets indicate the effectiveness of the proposed manifold network.

Geomorphology, Land-Use, and Hemeroby of Foothills in Colombian Orinoquia: Classification and Correlation at a Regional Scale

In the management of ecosystem services, it is significant to relate land use with the physical characteristics of the terrain, which allows establishing the conditioning factors of human activities and planning their distribution. These analyzes are based on thematic cartography, usually generated with visual classifications of satellite images. Traditional mapping techniques involve limiting the timely availability of information by taking extended periods for interpretation and integration of multiple data sets. This article presents a methodology to overcome these difficulties, implements machine learning and cloud computing to generate timely thematic cartography and spatial analysis to support land use planning. The study area was delimited according to altitudinal levels that define braided and anastomosed river systems. Acquisition, processing, and classification of input data for modeling were performed on the Google Earth Engine platform. The spatial correlation between hemeroby and geomorphology was calculated with the odds ratio and its respective confidence interval. Maps of 27 geomorphological units, 11 types of land use, and six hemeroby levels are presented at a scale of 1:50,000. Confusion matrices of implemented classification models were also reported, allowed evaluating global, user’s, and producer’s accuracy. Correlations between relict of natural areas with the structural environment and urban infrastructure with alluvial fans stand out. The information generated by these procedures is essential for planning land use and prioritizing the maintenance of ecosystem services.

Reduced Loss and Prevention of Substrate Modes with a Novel Coplanar Waveguide Based on Gap Waveguide Technology

The Gap Waveguide technology utilizes an Artificial Magnetic Conductor (AMC) to prevent the propagation of electromagnetic (EM) waves under certain conditions, resulting in various gap waveguide configurations. In this study, a novel combination of Gap Waveguide technology and the traditional coplanar waveguide (CPW) transmission line is introduced, analyzed, and demonstrated experimentally for the first time. This new line is referred to as GapCPW. Closed-form expressions for its characteristic impedance and effective permittivity are derived using traditional conformal mapping techniques. Eigenmode simulations using finite-element analysis are then performed to assess its low dispersion and loss characteristics. The proposed line demonstrates an effective suppression of the substrate modes in fractional bandwidths up to 90%. In addition, simulations show that a reduction of up to 20% of the dielectric loss can be achieved with respect to the traditional CPW. These features depend on the dimensions of the line. The paper concludes with the fabrication of a prototype and validation of the simulation results in the W band (75-110 GHz).

Pecora 22: Remote Sensing for Freshwater and Marine Environments

Pecora 22: Remote Sensing for Freshwater and Marine Environments

Comparative Analysis of the Results of Traditional and Digital Large-Scale Soil Mapping on the Example of a Site at the National Park “Smolenskoe Lakeland”

For the territory of the Smolensk Lakeland with a complex and understudied soil cover and considerable influence of the anthropogenic factor, it is relevant to create soil maps using the comparative geographic method. For an area of 8.8 km2 in the southwestern part of the Smolenskoye Lakeland National Park, soil maps (scale 1 : 25 000) were created using the methods of traditional and digital soil mapping (DSM). And a comparison of the results obtained was made. The territory under consideration is dominated by gray-humus soils (Umbric Cambisol (Loamic) and Arenosols (Ochric)) on two maps. These soils are associated with past agricultural use and regeneration of old arable soils. Smaller areas are occupied by alpha-humus and texture-differentiated soils (Podzols (Arenic) and Retisols (Loamic), respectively). In addition, isolated areas of psammozems (Arenosols) have been found and identified on the map using traditional approach. The overall accuracy of the map compiled by the DSM methods was 55%. The leading factors were identified: the position in the relief, the nature of the substrate and vegetation. The distribution of texturally differentiated soils associated with loamic parent materials and humus-peat soils formed in local depressions and on the floodplain of Lake Baklanovskoye is predicted with an accuracy of 87% and 60%, respectively. The location of podzol soils (Entic Rustic Podzols (Ochric)) is predicted with an accuracy of only 29%. The traditional mapping method was able to better reflect the diversity of the soil cover on a large scale compared to the digital method.

Comparative Analysis of the Results of Traditional and Digital Large-Scale Soil Mapping on the Example of a Key Site in the Smolenskoe Poozerye National Park

Comparative Analysis of the Results of Traditional and Digital Large-Scale Soil Mapping on the Example of a Key Site in the Smolenskoe Poozerye National Park

Magnetic Field Analysis of Surface-Mounted Permanent Magnet Motors Based on an Improved Conformal Mapping Method

In this article, an improved conformal mapping (ICM) method based on a complex potential function is proposed. It can improve the calculation speed and accuracy of magnetic fields for surface-mounted permanent magnet synchronous motors (SPMSMs). The eccentric-shaped poles are replaced by equivalent currents, and the improved conformal mapping method is adopted to transform an actual slotted motor air gap to an equivalent rectangular regular slotless air gap. The ICM method improves the speed of calculation because its number of mapping times is less than the traditional conformal mapping (TCM) method. By solving the complex potential generated by the equivalent current in the rectangular regular air-gap region, the air-gap magnetic field of the SPMSM under no-load, armature reaction and loaded conditions can be solved. Then, the no-load back-EMF and loaded torque performance under different load conditions are calculated. The accuracy of the ICM method is verified by finite-element simulation. In addition, the fast speed and high accuracy of the proposed ICM method incorporating the complex potential function for solving the air-gap magnetic field has been demonstrated in comparison to the TCM method with the Hague equation. An eccentric-shaped-pole permanent magnet motor prototype is also built and tested in experiment and the results agree with those calculated by the proposed ICM method.

Delineation of Wetland Areas in South Norway from Sentinel-2 Imagery and LiDAR Using TensorFlow, U-Net, and Google Earth Engine

Wetlands are important habitats for biodiversity and provide ecosystem services such as climate mitigation and carbon storage. The current wetland mapping techniques in Norway are tedious and costly, and remote sensing provides an opportunity for large-scale mapping and ecosystem accounting. We aimed to implement a deep learning approach to mapping wetlands with Sentinel-2 and LiDAR data over southern Norway. Our U-Net model, implemented through Google Earth Engine and TensorFlow, produced a wetland map with a balanced accuracy rate of 90.9% when validated against an independent ground-truth sample. This represents an improvement upon manually digitized land cover maps in Norway, which achieved accuracy rates of 46.8% (1:50,000 map) and 42.4% (1:5000 map). Using our map, we estimated a total wetland coverage area of 12.7% in southern Norway, which is double the previous benchmark estimates (5.6%). We followed an iterative model training and evaluation approach, which revealed that increasing the quantity and coverage of labeled wetlands greatly increases the model performance. We highlight the potential of satellite-based wetland maps for the ecosystem accounting of changes in wetland extents over time—something that is not feasible with traditional mapping methods.

Mapping Cropland Abandonment in Mountainous Areas in China Using the Google Earth Engine Platform

Knowledge about the spatial-temporal pattern of cropland abandonment is the premise for the management of abandoned croplands. Traditional mapping approaches of abandoned croplands usually utilize a multi-date classification-based land cover change trajectory. It requires quality training samples for land cover classification at each epoch, which is challenging in regions of smallholder agriculture in the absence of high-resolution imagery. Facing these challenges, a theoretical model is proposed to recognize abandoned croplands based on post-abandonment secondary succession. It applies the continuous change detection and classification (CCDC) temporal segmentation algorithm to Landsat time series (1986~2021) to obtain disjoint segments, representing croplands’ status. The post-abandonment secondary succession showing a greening trend is recognized using NDVI-based harmonic analysis, so as to capture its preceding abandonment. This algorithm is applied to a mountainous area in southwest China, where cropland abandonments are widespread. Validation based on stratified random samples referenced by a vegetation index time series and satellite images shows that the detected abandoned croplands have user accuracy, producer accuracy and an F1 score ranging from 43% to 71%, with variation among abandonment year. The study area has a potential cropland extent of 22,294 km2, within which 9252 km2 of the cropland was abandoned. The three peak years of abandonment were 1994, 2000, and 2011. The algorithm is suitable to be applied to large-scale mapping due to its automatic manner.

Visualization of critical metals in marine nodules by rapid and high-resolution LA-ICP-TOFMS mapping

Polymetallic nodules are one of the most ubiquitous critical metal reservoirs in the deep sea during early diagenesis. Trace elements including REY (Rare earth elements + Y), can provide a wealth of information to document the source-to-sink (STS) processes and critical metals enrichment regimes. Element imaging is an important “see-through” technique to display the distribution of multi-elements to understand the respective concentration relationship. However, traditional elemental mapping of macro (several millimeters) and micro (tens of micrometers) areas has remained a major challenge due to the long analytical time and low lateral resolution. Here, we apply inductively coupled plasma time-of-flight mass spectrometry (ICP-TOFMS), coupled to laser ablation (LA) with a low-dispersion two-volume sample cell and a dual concentric injector at both the macro- and micro-scales. LA-ICP-TOFMS mapping was applied to an entire polymetallic nodule (size of 10 * 9 mm2 @ 40 µm2 spot size, 1300 µm/s scan speed and 15 Hz frequency) and microdomains with microlayers (size of 700 * 350 µm2 @ 1 µm2, 200 µm/s and 200 Hz), respectively. The intra-nodule distribution patterns of elements are mainly controlled by the occurrence minerals. For example, the distribution patterns of some metals (e.g., Cu, Co, Ni) are similar to that of Mn, indicating these metals host in Mn-phase minerals. There is a trend of decreasing and then increasing of these elements from the inner to the outer layer, suggesting that the redox environment may have undergone a process from oxic to suboxic and then to oxic during the growth process. Our results demonstrate the feasibility of LA-ICP-TOFMS analysis on soft and porous materials, whose advantages include a shorter time and higher lateral resolution compared with traditional LA-ICP-QMS, expanding the geochemical mapping techniques for deep-sea early diagenetic sediments samples.

DIR-SLAM: Dynamic Interference Removal for Real-Time VSLAM in Dynamic Environments

Traditional visual simultaneous localization and mapping (VSLAM) systems mostly rely on the static-world assumption, which limits their applications in real-world scenarios with dynamic objects. When there are dynamic objects in the scene, the localization accuracy of the system decreases seriously. In this paper, in order to minimize the interference of dynamic objects in vision localization, we propose a real-time and robust dynamic interference removal (DIR) method, which is based on both prior knowledge and geometry information. Our approach employs a novel lightweight CNN network to output semantic labels and extends the semantics based on the correlations of descriptors to generate a segmented mask. We design a geometric consistency check module to remove the dynamic interference, which computes the bundle adjustment to predetermine the static keypoints, and then the semantic weighted epipolar constraint is used to identify the dynamic outliers. The proposed method is integrated into the front end of ORB-SLAM2 to filter out the dynamic keypoints which are associated with the known and unknown dynamic objects. We conduct experiments on the public TUM RGB-D dataset, the qualitative and quantitative results prove that the DIR method can improve the performance of the state-of-the-art VSLAM system in dynamic scenarios.

Transferability of Covariates to Predict Soil Organic Carbon in Cropland Soils

Precise knowledge about the soil organic carbon (SOC) content in cropland soils is one requirement to design and execute effective climate and food policies. In digital soil mapping (DSM), machine learning algorithms are used to predict soil properties from covariates derived from traditional soil mapping, digital elevation models, land use, and Earth observation (EO). However, such DSM models are trained for a specific dataset and region and have so far only allowed limited general statements to be made that would enable the models to be transferred to different regions. In this study, we test the transferability of SOC models for cropland soils using five different covariate groups: multispectral soil reflectance composites (satellite), soil legacy data (soil), digital elevation model derivatives (terrain), climate parameters (climate), and combined models (combined). The transferability was analyzed using data from two federal states in southern Germany: Bavaria and Baden-Wuerttemberg. First, baseline models were trained for each state with combined models performing best in both cases (R2 = 0.68/0.48). Next, the models were transferred and tested with soil samples from the other state whose data were not used during model calibration. Only satellite and combined models were transferable, but accuracy declined in both cases. In the final step, models were trained with samples from both states (mixed-data models) and applied to each state separately. This process significantly improved the accuracies of satellite, terrain, and combined models, while it showed no effect on climate models and decreased the models based on soil covariates. The experiment underlines the importance of EO for the transfer and extrapolation of DSM models.

Data-Driven Predictive Control of CVT System for Improving Energy Efficiency of Autonomous Vehicles

In this paper, the predictive data-driven control of Continuously Variable Transmission (CVT) systems is investigated to improve the energy efficiency of autonomous vehicles (AVs). Algorithms in AVs are expected to autonomously adapt and optimize themselves with different drive cycle conditions using the collected data during driving operation. We propose a Data Driven Control (DDC) framework for the speed ratio of CVTs in AVs which can learn the optimum pattern from archived real data. It includes a data archiving cycle in which the required DDC input/output signals of the vehicle are recorded. A DDC framework then runs in the driving cycle which includes a rolling optimization problem. More specifically, we use a predictive DDC algorithm to learn the optimal CVT speed ratio during the driving cycle instead of using the traditional pre-defined mapping. Using this framework, the CVT controller can adapt itself to any drive cycle by appropriately collecting data during the archiving cycle. The proposed control framework shows a satisfactory performance in HWFET and NEDC drive cycles. It reduces energy consumption by 1–5% compared to the pre-defined mappings.

The Gallery of Possibilities

Intersectionality and Black feminist thought invite cartographic thinking. Black feminist thinkers evoke cartographic metaphors and imaginaries like intersectionality and the matrix of domination to better illuminate and challenge systems of power and oppression. Such cartographic frameworks contribute toward an evolving feminist mapping toolkit and a growing expanse of feminist interventions in mapping. Yet, there remains a need to engage with the commitments of Black feminist thought in everyday mapping practices. I introduce the gallery of possibilities as a tangible methodological mapping intervention that materializes Black feminist thought and cartographic metaphors by retooling existing cartographic techniques, specifically small multiples, grounding and contextualizing them in questions within Black feminist thought. By engaging with intersecting systems of power and their operation, and the multiplicity of ways power emerges, the gallery of possibilities destabilizes the stasis encoded in traditional mapping solutions. I outline five core elements of the gallery of possibilities and illustrate the method with a case study—a workshop series on feminist map icons. In sum, the gallery of possibilities, along with Black feminist thought, expands the range and availability of feminist mapping methods and tools. Together, the gallery of possibilities and Black feminist thought transform everyday contemporary mapping practice.

Semantic stereo visual SLAM toward outdoor dynamic environments based on ORB-SLAM2

PurposeThe prerequisite for most traditional visual simultaneous localization and mapping (V-SLAM) algorithms is that most objects in the environment should be static or in low-speed locomotion. These algorithms rely on geometric information of the environment and restrict the application scenarios with dynamic objects. Semantic segmentation can be used to extract deep features from images to identify dynamic objects in the real world. Therefore, V-SLAM fused with semantic information can reduce the influence from dynamic objects and achieve higher accuracy. This paper aims to present a new semantic stereo V-SLAM method toward outdoor dynamic environments for more accurate pose estimation.Design/methodology/approachFirst, the Deeplabv3+ semantic segmentation model is adopted to recognize semantic information about dynamic objects in the outdoor scenes. Second, an approach that combines prior knowledge to determine the dynamic hierarchy of moveable objects is proposed, which depends on the pixel movement between frames. Finally, a semantic stereo V-SLAM based on ORB-SLAM2 to calculate accurate trajectory in dynamic environments is presented, which selects corresponding feature points on static regions and eliminates useless feature points on dynamic regions.FindingsThe proposed method is successfully verified on the public data set KITTI and ZED2 self-collected data set in the real world. The proposed V-SLAM system can extract the semantic information and track feature points steadily in dynamic environments. Absolute pose error and relative pose error are used to evaluate the feasibility of the proposed method. Experimental results show significant improvements in root mean square error and standard deviation error on both the KITTI data set and an unmanned aerial vehicle. That indicates this method can be effectively applied to outdoor environments.Originality/valueThe main contribution of this study is that a new semantic stereo V-SLAM method is proposed with greater robustness and stability, which reduces the impact of moving objects in dynamic scenes.

Genome-Wide Association Studies of Seven Root Traits in Soybean (Glycine max L.) Landraces.

Soybean [Glycine max (L.) Merr.], an important oilseed crop, is a low-cost source of protein and oil. In Southeast Asia and Africa, soybeans are widely cultivated for use as traditional food and feed and industrial purposes. Given the ongoing changes in global climate, developing crops that are resistant to climatic extremes and produce viable yields under predicted climatic conditions will be essential in the coming decades. To develop such crops, it will be necessary to gain a thorough understanding of the genetic basis of agronomic and plant root traits. As plant roots generally lie beneath the soil surface, detailed observations and phenotyping throughout plant development present several challenges, and thus the associated traits have tended to be ignored in genomics studies. In this study, we phenotyped 357 soybean landraces at the early vegetative (V2) growth stages and used a 180 K single-nucleotide polymorphism (SNP) soybean array in a genome-wide association study (GWAS) conducted to determine the phenotypic relationships among root traits, elucidate the genetic bases, and identify significant SNPs associated with root trait-controlling genomic regions/loci. A total of 112 significant SNP loci/regions were detected for seven root traits, and we identified 55 putative candidate genes considered to be the most promising. Our findings in this study indicate that a combined approach based on SNP array and GWAS analyses can be applied to unravel the genetic basis of complex root traits in soybean, and may provide an alternative high-resolution marker strategy to traditional bi-parental mapping. In addition, the identified SNPs, candidate genes, and diverse variations in the root traits of soybean landraces will serve as a valuable basis for further application in genetic studies and the breeding of climate-resilient soybeans characterized by improved root traits.

Spectroastrometric Reverberation Mapping of Broad-line Regions

Spectroastrometry measures source astrometry as a function of wavelength/velocity. Reverberations of spectroastrometric signals naturally arise in broad-line regions (BLRs) of active galactic nuclei (AGNs) as a result of the continuum variations that drive responses of the broad emission lines with time delays. Such signals provide a new diagnostic for mapping BLR kinematics and geometry, complementary to the traditional intensity reverberation mapping (RM) technique. We present a generic mathematical formalism for spectroastrometric RM and show that under realistic parameters of a phenomenological BLR model, the spectroastrometric reverberation signals vary on a level of several to tens of microarcseconds, depending on the BLR size, continuum variability, and angular-size distance. We also derive the analytical expressions of spectroastrometric RM for an inclined ring-like BLR. We develop a Bayesian framework with a sophisticated Monte Carlo sampling technique to analyze spectroastrometric data and infer the BLR properties, including the central black hole mass and angular-size distance. We demonstrate the potential of spectroastrometric RM in spatially resolving BLR kinematics and geometry through a suite of simulation tests. The application to realistic observation data of 3C 273 obtains tentative, but enlightening results, reinforcing the practical feasibility of conducting spectroastrometric RM experiments on bright AGNs with the operating Very Large Telescope Interferometer as well as possibly with the planned next-generation 30 m class telescopes.

Comparison of Sentinel Lymph Node Biopsy by Blue Dye Conjunction With Indocyanine Green or Radioisotope in Early-Stage Breast Cancer: A Prospective Single-Center Observational Study.

Although radioisotope (RI) combined with blue dye (BD) is the standard technique for sentinel lymph node (SLN) biopsy in breast cancer, the use of RI is limited at some institutions due to the specific equipment needed. Indocyanine green (ICG) fluorescence detection has been developed as a potential substitute for RI method. However, reports on the sensitivity of ICG and RI techniques in detecting SLN are inconsistent; hence, the present study was designed to compare the clinical efficacy between the combined method of ICG + BD (ICG-B) and RI + BD (RI-B). A prospective observational study was performed that identified 138 breast cancer patients who had undergone lymphatic mapping and SLN biopsy with ICG-B or RI-B. The SLN detection rate, positive SLN counts, and lymph node metastasis between the 2 groups were compared. A total of 71 patients were recruited in the ICG-B group, while 67 were recruited in the RI-B group. The SLN detection rate was 100% in both the ICG-B and RI-B groups. Lymph node metastasis was found in 13 patients using ICG-B and in 12 patients using the RI-B technique (18.31% vs 17.91%, respectively; P = .61). No significant differences were observed in the positive SLN counts (3.12 ± 2.01 vs 3.33 ± 2.24, P = .37) between the 2 groups. Indocyanine green combined with BD has an equal efficacy compared with RI plus BD when performing an axillary SLN biopsy in breast cancer. The ICG plus BD procedure is a promising alternative to traditional standard mapping methods.