High-quality Maps Research Articles

Navigator-gated free-breathing 2D radial cardiac joint T1-T2 mapping.

To implement a navigator-gated free-breathing 2D radial joint T1-T2 mapping technique for the myocardium at 3T, and to characterize the impact of the navigator rejection on the precision and accuracy of the T1 and T2 maps. The proposed technique, named PARMANav (for PArametric Radial Mapping with Navigator gating), collects 25 lung-liver navigator-gated electrocardiogram (ECG)-triggered single-shot radial gradient-recalled echo (GRE) images with five magnetization preparations. Source images were reconstructed using compressed sensing. Extended-phase-graph simulations were used to generate an acquisition-specific joint T1-T2 dictionary. The impact of the number of rejected navigators on the relaxation times was assessed in numerical simulations. The influence of the navigator acceptance window width (NAWW) and heart rate on the relaxation times was assessed in phantom studies, 10 healthy volunteers, and 3 patients. The relaxation times were compared to routine T1 and T2 mapping values. The numerical simulations showed negligible dependence on the number of rejected navigators (<6% T1-T2 variation). In the phantom, PARMANav T1-T2 values were stable across heart rates: the T1-T2 coefficient of variation (CoV) was <3%. As expected from literature, in-vivo PARMANav T1-T2 values were higher than routine values (T1 = 1331 ± 53 ms, T2 = 46.1 ± 2.5 ms vs. T1 = 1095 ± 81 ms, T2 = 38.7 ± 2.9 ms, p < 0.001), while the PARMANav T2 CoV was significantly reduced. No myocardial T1-T2 values or CoV trend was observed for the different NAWW. Feasibility in patients was demonstrated, where high-quality maps were obtained. PARMANav allows for precise and accurate joint T1-T2 mapping without requiring breath holding. Through-plane motion artifacts were avoided with a navigator that did not impact the accuracy or precision of the resulting maps.

The Applications of AI Tools in the Fields of Weather and Climate—Selected Examples

Large language models (LLMs) based on artificial intelligence have found applications across various sectors—including medicine, education, science, literature, and marketing. Although they offer considerable opportunities, their limitations also raise important concerns. This study evaluates several AI tools in the context of meteorology and climatology. The tools examined include ChatGPT o3-mini, o1, 4.o, 4.0; Gemini Advanced 1.5 and 2.0; Copilot; Perplexity; DataAnalyst; Consensus; ScholarGPT; SciSpace; Claude; and DeepSeek. The evaluation tasks comprised cloud recognition and classification from photographs, gap-filling in literature reviews, map creation based on provided datasets, comparative interpretation of maps, and archival data retrieval from line graphs converted to numerical data. Each task was rated on a 0–5 scale. Conducted between February 2024 and February 2025, the study found that ChatGPT o3-mini excelled in cloud classification; ChatGPT4.o and ScholarGPT produced high-quality maps; Claude 3.5 Sonnet and SciSpace provided the most detailed map descriptions; and Consensus and ChatGPT o1 were the most effective for literature review support. However, all tools performed poorly in regards to archival data retrieval, with Claude 3.5 Sonnet yielding the smallest errors. Overall, substantial progress was observed over the study period.

Mapping COVID-19: Applying Ethical Strategies in Web Mapping Decisions

In December 2019, the world was introduced to a coronavirus, the likes of which have never been seen before, resulting in the COVID-19 pandemic. During the initial months of the pandemic, academics, government agencies, and concerned citizens, among others, raced to generate maps to help explain the geographic spread of the virus over space and time. Maps, however, can be intentionally or unintentionally misleading; we saw this play out during the pandemic as we rushed to make COVID-19 dashboards and online maps. While the Johns Hopkins University’s COVID-19 dashboard served as the first authoritative resource on the pandemic, it lacked the spatial granularity required for smaller states, such as Connecticut. Realizing this limitation, a team of geography Ph.D. students and faculty came together to develop a dashboard better suited to serve the residents of the state and aid policymakers during a time when each decision could have vast consequences. This case study reflects on our team’s strategies to address the ethical considerations to deliver high-quality maps and to promote healthy skepticism among users of the COVID-19 dashboard. We first provide a historical background of the discussion around ethics in cartography that we used to frame our arguments and strategies. Second, we summarize our deliverables, starting with an online interactive dashboard for 169 towns suitable for both mobile and desktop viewing. For the visualizations, we tried to balance the right amount of health information so that they were cartographically sound, easy to understand, and not misleading. As geographers, this is one of our responsibilities to our communities.

METHODOLOGY OF THE NUMERICAL MODELLING OF PARAMETERS DETERMINATION OF THE RADIOMETRIC IMAGE OF REMOTE EARTH SURFACE SENSING BY BISTATIC RADIOMETER

The on-board bistatic radiometer (BR) provides sufficiently high-quality mapping of various terrestrial surfaces. The subject of study in the article is the determination of parameters of radiometric images (RI) of earth surfaces based on the BR construction options that are technically feasible. The aim of the research is to develop a methodology for numerical modelling of RI parameters calculation during remote sensing of the earth's surfaces by BR. Tasks: To consider the range of the radar, accounting for image bands, detection, and measurement of course difference, and resolution along the carrier's flight path and across its path. The task also includes the calculation of the difference Doppler frequency correction (DDFC) of payload signals, root mean square deviations (RMSD) of pitch difference errors and DDFC errors. Methods used: analysis and synthesis of the obtained output RI parameters according to possible construction options. The following results were obtained. A methodology for numerical modelling of RI parameters determination during remote sensing of a low-contrast payload such as grass-concrete is developed. The AN-14 ‘Bee’ aircraft was chosen as a carrier for the BR, and the 22 GHz bandwidth of the sub-waveband was chosen for the BR 3 mm. The analysis was carried out for six possible variants of the mapping system. The ranges of the systems, the values of image bands, and the DDFC of objects are calculated, considering the accepted technical characteristics of the radar and the influence of the atmosphere. The RI characteristics at a probability of correct detection of 0.5 are presented. The image parameters for measuring the differences in the stroke and DDFC of mapping objects are given. The number of pixels (pitch differences) in the image row, the resolution along the carrier path, across the path, and at the DDFC were calculated. The number of Doppler filters for each pitch difference, the RMSD of the pitch difference measurement, and the RMSD of the object's DDFC measurement were obtained. The values of the fluctuation sensitivity of the BR were calculated for the following design variants. Conclusions. Based on the results of numerical modelling, the analysis of the information capacity per RI during remote sensing of earth surfaces by an onboard system that is spaced apart is carried out. A promising area for further research may be the introduction of a very weak broadband noise sensing signal into the mapping system and the assessment of the impact of interference that is spatially correlated in the BR.

RT-FLOW: FPGA Implementation of Real-Time Optical-Flow-Based SLAM for High-Speed Tracking and High-Quality Mapping

RT-FLOW: FPGA Implementation of Real-Time Optical-Flow-Based SLAM for High-Speed Tracking and High-Quality Mapping

VISUALIZATION AND MAPPING OF HOUSEHOLD HOUSING CONDITIONS IN WEST JAVA USING MULTIDIMENSIONAL SCALING

This study aims to map household housing conditions in West Java using the Multidimensional Scaling (MDS) approach. West Java, as the most populous province in Indonesia, faces significant challenges regarding housing inequalities, infrastructure access, and socio-economic disparities between urban and rural areas. These disparities necessitate a comprehensive and systematic approach to identify vulnerable regions and inform targeted policy interventions. Using data from the 2023 National Socio-Economic Survey (Susenas), this study analyzes five main groups of variables: basic needs, housing facilities and ownership, socio-economic status, access to services and infrastructure, and household demographics and welfare. The Multidimensional Scaling (MDS) technique is employed due to its capability to reduce complex, high-dimensional data into a two-dimensional representation, allowing clearer visualization of regional disparities and interrelationships among variables. MDS also facilitates robust model evaluation, ensuring high-quality mapping results. The MDS results reveal significant variations in household conditions, with urban areas such as Bekasi and Depok City showing better infrastructure access and welfare outcomes compared to rural areas like Cirebon and Sukabumi District. Evaluation of the MDS model indicates excellent performance, with STRESS values ranging from 0.042 to 0.083 and RSQ values between 0.993 and 0.999, demonstrating high accuracy. This study addresses a research gap where few studies have comprehensively mapped housing inequalities in large, diverse regions like West Java using advanced multidimensional techniques. The findings emphasize the importance of policies focusing on infrastructure development and equitable distribution of social assistance in underdeveloped regions to reduce regional disparities.

Mapping surface soil organic carbon density of cultivated land using machine learning in Zhengzhou

Research on soil organic carbon (SOC) is crucial for improving soil carbon sinks and achieving the "double-carbon" goal. This study introduces ten auxiliary variables based on the data from a 2021 land quality survey in Zhengzhou and a multi-objective regional geochemical survey. It uses geostatistical ordinary kriging (OK) interpolation, as well as classical machine learning (ML) models, including random forest (RF) and support vector machine (SVM), to map soil organic carbon density (SOCD) in the topsoil layer (0 − 20 cm) of cultivated land. It partitions the sampling data to assess the generalization capability of the machine learning models, with Zhongmu County designated as an independent test set (dataset2) and the remaining data as the training set (dataset1). The three models are trained using dataset1, and the trained machine learning models are directly applied to dataset2 to evaluate and compare their generalization performance. The distribution of SOCD and SOCS in soils of various types and textures is analyzed using the optimal interpolation method. The results indicated that: (1) The average SOC densities predicted by OK interpolation, RF, and SVM are 3.70, 3.74, and 3.63 kg/m2, with test set precisions (R2) of 0.34, 0.60, and 0.81, respectively. (2) ML achieves a significantly higher predictive precision than traditional OK interpolation. The RF model's precision is 0.21 higher than the SVM model and more precise in estimating carbon stock. (3) When applied to the dataset2, the RF model exhibited superior generalization capabilities (R2 = 0.52, MSE = 0.32) over the SVM model (R2 = 0.32, MSE = 0.45). (4) The spatial distribution of surface SOCD in the study area exhibits a decreasing gradient from west to east and from south to north. The total carbon stock in the study area is estimated at approximately 10.76 × 106t. (5) The integration of soil attribute variables, climatic variables, remote sensing data, and machine learning techniques holds significant promise for the high-precision and high-quality mapping of soil organic carbon density (SOCD) in agricultural soils.

GAN-Based Map Generation Technique of Aerial Image Using Residual Blocks and Canny Edge Detector

As the significance of meticulous and precise map creation grows in modern Geographic Information Systems (GISs), urban planning, disaster response, and other domains, the necessity for sophisticated map generation technology has become increasingly evident. In response to this demand, this paper puts forward a technique based on Generative Adversarial Networks (GANs) for converting aerial imagery into high-quality maps. The proposed method, comprising a generator and a discriminator, introduces novel strategies to overcome existing challenges; namely, the use of a Canny edge detector and Residual Blocks. The proposed loss function enhances the generator’s performance by assigning greater weight to edge regions using the Canny edge map and eliminating superfluous information. This approach enhances the visual quality of the generated maps and ensures the accurate capture of fine details. The experimental results demonstrate that this method generates maps of superior visual quality, achieving outstanding performance compared to existing methodologies. The results show that the proposed technology has significant potential for practical applications in a range of real-world scenarios.

GPR Image Retrieval and Localization Based on Spatial Prior Constraints

Abstract Localizing Ground Penetrating Radar (LGPR) offers the distinct advantage of being unaffected by weather and light conditions changes. As a novel auxiliary driving localizing system, LGPR enhances the robustness of the auto-drive system. Traditional ground penetrating radar localizing technologies rely heavily on high-quality map data, exhibiting optimal performance in environments with complete, high-precision maps. However, deviations from the mapped trajectory can compromise localization accuracy. We introduce a novel approach: a ground penetrating radar image retrieval and localization method based on spatial prior constraints of array antennas to address this challenge. This method aims to mitigate mismatches in ground penetrating radar navigation and localization encountered under incomplete map coverage. We performed a comparative analysis with the LGPR baseline and then validated them experimentally on our dataset. The results show that the proposed method achieved significant enhancement in accuracy. In particular, the superiority of the proposed method is demonstrated in low overlap GPR image matching and localisation.

A temporal-spatial deep learning network for winter wheat mapping using time-series Sentinel-2 imagery

A temporal-spatial deep learning network for winter wheat mapping using time-series Sentinel-2 imagery

Enhanced Path Planning and Obstacle Avoidance Based on High-Precision Mapping and Positioning.

High-precision positioning and multi-target detection have been proposed as key technologies for robotic path planning and obstacle avoidance. First, the Cartographer algorithm was used to generate high-quality maps. Then, the iterative nearest point (ICP) and the occupation probability algorithms were combined to scan and match the local point cloud, and the positions and attitudes of the robot were obtained. Furthermore, Sparse Matrix Pose Optimization was carried out to improve the positioning accuracy. The positioning accuracy of the robot in x and y directions was kept within 5 cm, the angle error was controlled within 2°, and the positioning time was reduced by 40%. An improved timing elastic band (TEB) algorithm was proposed to guide the robot to move safely and smoothly. A critical factor was introduced to adjust the distance between the waypoints and the obstacle, generating a safer trajectory, and increasing the constraint of acceleration and end speed; thus, smooth navigation of the robot to the target point was achieved. The experimental results showed that, in the case of multiple obstacles being present, the robot could choose the path with fewer obstacles, and the robot moved smoothly when facing turns and approaching the target point by reducing its overshoot. The proposed mapping, positioning, and improved TEB algorithms were effective for high-precision positioning and efficient multi-target detection.

A method for crystallographic mapping of an alpha-beta titanium alloy with nanometre resolution using scanning precession electron diffraction and open-source software libraries.

An approach for the crystallographic mapping of two-phase alloys on the nanoscale using a combination of scanned precession electron diffraction and open-source python libraries is introduced in this paper. This method is demonstrated using the example of a two-phaseα/β titanium alloy. The data were recorded using a direct electron detector to collect the patterns, and recently developed algorithms to perform automated indexing and analyse the crystallography from the results. Very high-quality mapping is achieved at a 3nm step size. The results show the expected Burgers orientation relationships between the α laths and β matrix, as well as the expected misorientations betweenα laths. A minor issue was found that one area was affected by 180° ambiguities in indexing occur due to this area being aligned too close to a zone axis of the α with twofold projection symmetry (not present in 3D) in the zero-order Laue Zone, and this should be avoided in data acquisition in the future. Nevertheless, this study demonstrates a good workflow for the analysis of nanocrystalline two- or multi-phase materials, which will be of widespread use in analysing two-phase titanium and other systems and how they evolve as a function of thermomechanical treatments.

A hyperspectral view of the nearshore Mississippi River Delta: Characterizing suspended particles in coastal wetlands using imaging spectroscopy

A hyperspectral view of the nearshore Mississippi River Delta: Characterizing suspended particles in coastal wetlands using imaging spectroscopy

Review of Deep Learning Approaches for Interleaved Photoacoustic and Ultrasound (PAUS) Imaging.

Photoacoustic (PA) imaging provides optical contrast at relatively large depths within the human body, compared to other optical methods, at ultrasound (US) spatial resolution. By integrating real-time PA and US (PAUS) modalities, PAUS imaging has the potential to become a routine clinical modality bringing the molecular sensitivity of optics to medical US imaging. For applications where the full capabilities of clinical US scanners must be maintained in PAUS, conventional limited view and bandwidth transducers must be used. This approach, however, cannot provide high-quality maps of PA sources, especially vascular structures. Deep learning (DL) using data-driven modeling with minimal human design has been very effective in medical imaging, medical data analysis, and disease diagnosis, and has the potential to overcome many of the technical limitations of current PAUS imaging systems. The primary purpose of this article is to summarize the background and current status of DL applications in PAUS imaging. It also looks beyond current approaches to identify remaining challenges and opportunities for robust translation of PAUS technologies to the clinic.

Determining subsurface temperature & lithospheric structure from joint geophysical-petrological inversion: A case study from Ireland

Determining subsurface temperature & lithospheric structure from joint geophysical-petrological inversion: A case study from Ireland

Multipath Planning Acceleration Method With Double Deep R-Learning Based on a Genetic Algorithm

Autonomous navigation is a well-studied field in robotics requiring high standards of efficiency and reliability. Many studies focus on applying AI techniques to obtain a high-quality map, a precise localization, or improve the proposed trajectory to be followed by the agent. As traditional planning methods need a high-quality map to obtain optimal trajectories, this paper addresses the problem of multipath map-less planning, and proposes a novel multipath planning algorithm (Double Deep Reinforcement Learning - Enhanced Genetic (DDRL-EG)) for mobile robots in an unknown environment. It combines Double Deep Reinforcement Learning (DDRL) with Heuristic Knowledge (HK), Experience Replay (ER), Genetic Algorithm (GA), and Dynamic Programming (DP), allowing the agent to reach its target successfully without maps. In addition, it optimizes the training time and the chosen path in terms of time and distance to the target. A hybrid method is also used in which Semi-Uniform Distributed Exploration (SUDE) is employed to determine the probability that the action is decided based on directed knowledge, hybrid knowledge, or autonomous knowledge. The performance of DDRL-EG is compared with two other algorithms in two different environments. The results show that DDRL-EG is a more robust and powerful algorithm since with less training, it can provide much smoother and shorter trajectories to the target.

Visual preferences in map label placement

Digital maps are important for many decision-making tasks that require situational awareness, navigation, or location-specific data. Often, digital mapping tools must generate a map that displays labels near associated features in a visually appealing manner, without occluding important information. Automated label placement systems generally accomplish this nontrivial task through a combination of heuristic algorithms and cartography rules, but the resulting maps often do not reflect the preferences and needs of the map user. To achieve higher quality map views, research is needed to identify cognitive and computational approaches for generating high-quality maps that meet user needs and expectations. In this paper, we present a study that explores the visual preferences of map users and supports the development of a preference model for digital map displays. In particular, we found that participants demonstrated consistent preferences for how labels are placed near their point of interest, and that they were more likely to choose positions that prioritized alignment over distance when ranking labels that made trade-offs between them.

Analyzing the syntax and salience of causal links embedded within semantic links in concept maps: Implications for temporal flow and learning transfer

Including causal links in concept maps enables learners to meaningfully relate concepts to a larger context or problem in terms of how and where concepts apply within the chains of causal events that lead to a given goal or outcome. Given that higher quality maps are produced when students link and sequence events to flow temporally and sequentially in a consistent direction towards a target outcome in a map, it is highly plausible that students can improve learning transfer (the ability to apply concepts to diagnose and solve problems) by including and making more salient the sequences of causal links nestled in the semantic links in concept maps. To lay the groundwork to empirically test this proposition in future research, this study: 1) analyzes 16 concept maps presented in the Proceedings of the 8th Int. Conference on Concept Mapping to codify the diverse and sometimes incongruent syntaxes used to convey causal relationships; 2) examines how the causal link syntaxes combined with semantic links work jointly (or against each other) to create temporal flow; and 3) explore how causal and semantic links can be integrated to increase the saliency and quality of the causal networks connecting concepts to outcomes. A better understanding of how causal links are expressed, integrated, and made more salient in concept maps can reveal ways to help students create concept maps that are more accurate, meaningful, and effective in improving the ability to apply concepts to solve complex problems.

Computationally Efficient DNN Mapping Search Heuristic using Deep Reinforcement Learning

In this work, we present a computationally efficient Reinforcement Learning mapping search heuristic for finding high quality mappings for N-dimensional convolution loops that uses a computationally inexpensive reward function based on potential data reuse of operands to guide the search process. We also present a RL state representation generalizable to N-dimensional convolution loops, and a state representation parsing strategy ensuring that only valid mappings are evaluated for quality. Our RL search heuristic is applicable to multi-core systems with a memory hierarchy. We show that our RL based search heuristic for a range of 3D convolution layers, at significantly lower computational expense than random search, generally yields mappings with lower Energy-Delay Product (EDP) for an architecture with multiple processing elements with shared memory connected to DRAM. Our evaluation results demonstrated across 19 3D convolution layers, shows that our RL method performed only an average 11.24% of the operations of that of Timeloop’s random search for assessing same number of valid mappings. The mappings found using Timeloop had an average 12.51% higher EDP compared to lowest EDP mapping found using our RL method. Further, the lowest EDP mappings found using our method had an average only 4.69× higher EDP than the theoretical lower bound EDP, with the best case being only 1.29× higher.

New insights on the photocomplex of Roseiflexus castenholzii revealed from comparisons of native and carotenoid-depleted complexes

In wild-type phototrophic organisms, carotenoids (Crts) are primarily packed into specific pigment-protein complexes along with (Bacterio)chlorophylls and play important roles in the photosynthesis. Diphenylamine (DPA) inhibits carotenogenesis but not phototrophic growth of anoxygenic phototrophs and eliminates virtually all Crts from photocomplexes. To investigate the effect of Crts on assembly of the reaction center-light-harvesting (RC-LH) complex from the filamentous anoxygenic phototroph Roseiflexus (Rfl.) castenholzii, we generated carotenoidless (Crt-less) RC-LH complexes by growing cells in the presence of DPA. Here, we present cryo-EM structures of the Rfl. castenholzii native and Crt-less RC-LH complexes with resolutions of 2.86Å and 2.85Å, respectively. From the high-quality map obtained, several important but previously unresolved details in the Rfl. castenholzii RC-LH structure were determined unambiguously including the assignment and likely function of three small polypeptides, and the content and spatial arrangement of Crts with bacteriochlorophyll molecules. The overall structures of Crt-containing and Crt-less complexes are similar. However, structural comparisons showed that only five Crts remain in complexes from DPA-treated cells and that the subunit X (TMx) flanked on the N-terminal helix of the Cyt-subunit is missing. Based on these results, the function of Crts in the assembly of the Rfl. castenholzii RC-LH complex and the molecular mechanism of quinone exchange is discussed. These structural details provide a fresh look at the photosynthetic apparatus of an evolutionary ancient phototroph as well as new insights into the importance of Crts for proper assembly and functioning of the RC-LH complex.