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In this paper, I propose a new way of defining sport that I call a 'core-periphery' model. According to a core-periphery model, sport comes in degrees – what I refer to as 'sport-likeness' – and the aim of the philosopher of sport is to chart those dimensions along which an activity can be more or less a sport. By introducing the concept of sport-likeness, the core-periphery model complicates the picture of what is or is not a sport and encourages philosophers interested in defining sport to engage with the social sciences in exploring the extension of the term sport in common usage. In this paper I present the results of a small survey about attitudes to sport, and use it to illustrate how a core-periphery definition of sport would proceed. [ABSTRACT FROM AUTHOR]

Airborne radar forward-looking imaging holds significant promise for applications such as autonomous navigation, battlefield reconnaissance, and terrain mapping. However, traditional methods are hindered by complex system design, azimuth ambiguity, and low resolution. This paper introduces a distributed array collaborative, forward-looking imaging approach, where multiple aircraft with linear arrays fly in parallel to achieve coherent imaging. We analyze signal model characteristics and highlight the limitations of conventional algorithms. To address these issues, we propose a high-resolution imaging algorithm that combines an enhanced missing-data iterative adaptive approach with aperture interpolation technique (MIAA-AIT) for effective signal recovery in distributed arrays. Additionally, a novel reference range cell migration correction (reference RCMC) is employed for precise range–azimuth decoupling. The forward-looking algorithm effectively transforms distributed arrays into a virtual long-aperture array, enabling high-resolution, high signal-to-noise ratio imaging with a single snapshot. Simulations and real data tests demonstrate that our method not only improves resolution but also offers flexible array configurations and robust performance in practical applications. [ABSTRACT FROM AUTHOR]

Filtering approaches on Global Ecosystem Dynamics Investigation (GEDI) data differ considerably across existing studies and it is yet unclear which method is the most effective. We conducted an in‐depth analysis of GEDI's vertical accuracy in mapping terrain and canopy heights across three study sites in temperate forests and grasslands in Spain, California, and New Zealand. We started with unfiltered data (2,081,108 footprints) and describe a workflow for data filtering using Level 2A parameters and for geolocation error mitigation. We found that retaining observations with at least one detected mode eliminates noise more effectively than sensitivity. The accuracy of terrain and canopy height observations depended considerably on the number of modes, beam sensitivity, landcover, and terrain slope. In dense forests, a minimum sensitivity of 0.9 was required, while in areas with sparse vegetation, sensitivity of 0.5 sufficed. Sensitivity greater than 0.9 resulted in an overestimation of canopy height in grasslands, especially on steep slopes, where high sensitivity led to the detection of multiple modes. We suggest excluding observations with more than five modes in grasslands. We found that the most effective strategy for filtering low‐quality observations was to combine the quality flag and difference from TanDEM‐X, striking an optimal balance between eliminating poor‐quality data and preserving a maximum number of high‐quality observations. Positional shifts improved the accuracy of GEDI terrain estimates but not of vegetation height estimates. Our findings guide users to an easy way of processing of GEDI footprints, enabling the use of the most accurate data and leading to more reliable applications. Plain Language Summary: The Global Ecosystem Dynamics Investigation (GEDI) collected terrain and canopy observations using laser altimetry. The quality of terrain and canopy observations is influenced by acquisition conditions and land (cover) characteristics. Consequently, a considerable amount of GEDI observations is discarded as noise, and further filtering is necessary to retain only high‐quality observations. Our objective was to assess how environmental and acquisition characteristics influence the accuracy of terrain and canopy height of GEDI observations. Although the main objective of the GEDI mission was to map forests, we also focused on grasslands. GEDI serves not only as an essential source of information on canopy height but also provides accurate terrain observations. Furthermore, it is important to know that GEDI does not overestimate the height of low vegetation as this can result in an overestimation of carbon storage. We distinguished four steps in the GEDI data processing: (a) removal of noise observations, (b) removal of low‐quality data, (c) effect of additional acquisition characteristics, and (d) mitigation of geolocation error. We found that the accuracy of terrain and canopy height observations depended considerably on the number of detected modes, beam sensitivity, landcover, and terrain slope. Key Points: Terrain is crucial for estimates of canopy height, however only 20%–30% of footprints have an absolute error of terrain estimates <3 mThe quality of terrain and canopy height estimates depends on the interplay of number of modes, sensitivity, land cover, and terrain slopeNoise and low‐quality footprints can be successfully removed using number of modes, sensitivity, quality flag and difference from TanDEM‐X [ABSTRACT FROM AUTHOR]

Recent advancements in deep learning have significantly improved the performance of remote sensing scene classification, a critical task in remote sensing applications. This study presents a new aerial scene classification model, the Multi-Scale Swin–CNN Aerial Classifier (MSCAC), which employs the Swin Transformer, an advanced architecture that has demonstrated exceptional performance in a range of computer vision applications. The Swin Transformer leverages shifted window mechanisms to efficiently model long-range dependencies and local features in images, making it particularly suitable for the complex and varied textures in aerial imagery. The model is designed to capture intricate spatial hierarchies and diverse scene characteristics at multiple scales. A framework is developed that integrates the Swin Transformer with a multi-scale strategy, enabling the extraction of robust features from aerial images of different resolutions and contexts. This approach allows the model to effectively learn from both global structures and fine-grained details, which is crucial for accurate scene classification. The model's performance is evaluated on several benchmark datasets, including UC-Merced, WHU-RS19, RSSCN7, and AID, where it demonstrates a superior or comparable accuracy to state-of-the-art models. The MSCAC model's adaptability to varying amounts of training data and its ability to improve with increased data make it a promising tool for real-world remote sensing applications. This study highlights the potential of integrating advanced deep-learning architectures like the Swin Transformer into aerial scene classification, paving the way for more sophisticated and accurate remote sensing systems. The findings suggest that the proposed model has significant potential for various remote sensing applications, including land cover mapping, urban planning, and environmental monitoring. [ABSTRACT FROM AUTHOR]

Image stitching aims to construct a wide field of view with high spatial resolution, which cannot be achieved in a single exposure. Typically, conventional image stitching techniques, other than deep learning, require complex computation and are thus computationally expensive, especially for stitching large raw images. In this study, inspired by the multiscale feature of fluid turbulence, we developed a fast feature point detection algorithm named local-peak scale-invariant feature transform (LP-SIFT), based on the multiscale local peaks and scale-invariant feature transform method. By combining LP-SIFT and RANSAC in image stitching, the stitching speed can be improved by orders compared with the original SIFT method. Benefiting from the adjustable size of the interrogation window, the LP-SIFT algorithm demonstrates comparable or even less stitching time than the other commonly used algorithms, while achieving comparable or even better stitching results. Nine large images (over 2600 × 1600 pixels), arranged randomly without prior knowledge, can be stitched within 158.94 s. The algorithm is highly practical for applications requiring a wide field of view in diverse application scenes, e.g., terrain mapping, biological analysis, and even criminal investigation. [ABSTRACT FROM AUTHOR]

This study focused on applying numerical simulations to assess damaged areas caused by debris flows, employing the LS-RAPID program while emphasizing the importance of terrain information. Terrain information used in the numerical simulation included a 1:5000 digital terrain map and a digital surface model using an unmanned aerial vehicle. Quantification of the amount of soil that collapsed from the road embankment slope, which is the source of the debris flow, facilitated the computation of the debris flow that closely resembled real-world conditions. In particular, incorporating the high-resolution digital surface model (DSM) with 3-cm topographic information resulted in an interpretation of the actual soil flow damage range that is similar to actual observations of the digital elevation model (DEM), which had 1-m grid topographic information. This difference arises from DSM as it reflects the information of low hills downstream. The range of damage changed as the direction of the debris flow changed because of the low hill. Many variables need adjustment for the accuracy of debris flow numerical simulation. However, the direction and range of flow vary greatly depending on topographic information, highlighting the necessity of applying high-resolution terrain information. The results of debris flow simulations with high-resolution terrain information are expected to improve accuracy and help prepare risk or damage maps. [ABSTRACT FROM AUTHOR]

Search and rescue (SaR) is challenging, due to the unknown environmental situation after disasters occur. Robotics has become indispensable for precise mapping of the environment and for locating the victims. Combining flying and ground robots more effectively serves this purpose, due to their complementary features in terms of viewpoint and maneuvering. To this end, a novel, cost-effective framework for mapping unknown environments is introduced that leverages You Only Look Once and video streams transmitted by a ground and a flying robot. The integrated mapping approach is for performing three crucial SaR tasks: localizing the victims, i.e., determining their position in the environment and their body pose, tracking the moving victims, and providing a map of the ground elevation that assists both the ground robot and the SaR crew in navigating the SaR environment. In real-life experiments at the CyberZoo of the Delft University of Technology, the framework proved very effective and precise for all these tasks, particularly in occluded and complex environments. [ABSTRACT FROM AUTHOR]

In order to solve the problems of low work efficiency, poor measurement accuracy, and high safety hazards of traditional surveying and mapping techniques in railway construction in high-altitude areas, by using a vertical fixed-wing UAV flight platform equipped with an oblique photography camera, a complete aerial photogrammetry technology workflow was established based on the terrain characteristics and survey requirements of high-altitude areas. The use of UAV oblique photogrammetry technology to complete railway field survey work could effectively reduce the field workload of railway survey in high-altitude areas. From the comparison and analysis of checkpoint data, it can be seen that the MSE in the plane of the checkpoint is 0.06 m, and the MSE in the elevation is 0.12 m, which meets the accuracy requirements of topographic maps and real scene 3D models. The research results indicate that UAV oblique photogrammetry technology can provide production projects with high-definition images, terrain map data, and 3D models. UAV have advantages such as mobility, flexibility, and are not limited by terrain and features, which can improve production efficiency, reduce operating costs, and significantly reduce safety hazards in high-altitude areas. [ABSTRACT FROM AUTHOR]

Drone area coverage primarily involves using unmanned aerial vehicles (UAVs) for extensive monitoring, surveying, communication, and other tasks over specific regions. The significance and value of this technology are multifaceted. Firstly, UAVs can rapidly and efficiently reach remote or inaccessible areas to perform tasks such as terrain mapping, disaster monitoring, or search and rescue, significantly enhancing response speed and execution efficiency. Secondly, drone area coverage in agricultural monitoring, forestry conservation, and urban planning offers high-precision data support, aiding scientists and decision-makers in making more accurate judgments and decisions. Additionally, drones can serve as temporary communication base stations in areas with poor communication, ensuring the transfer of crucial information. Drone area coverage technology is vital in improving work efficiency, reducing costs, and strengthening decision support. This paper aims to solve the optimization problem of multi-UAV area coverage flight path planning to enhance system efficiency and task execution capability. For multi-center optimization problems, a region decomposition method based on the Voronoi graph is designed, transforming the multi-UAV area coverage issue into the single-UAV area coverage problem, greatly simplifying the complexity and computational process. For the single-UAV area coverage problem and its corresponding area, this paper contrives a convolutional neural network with the channel and spatial attention mechanism (CSAM) to enhance feature fusion capability, enabling the model to focus on core features for solving single-UAV path selection and ultimately generating the optimal path. Simulation results demonstrate that the proposed method achieves excellent performance. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to carry out a systematic literature review (SLR) on the integration of Lean, Industry 4.0 and the supply chain or the Lean Supply Chain (LSC) 4.0. The research analyses the current research on the LSC 4.0 concept in an increasingly digitalised world. The authors present the benefits, motivations, critical success factors and challenges of integrating the LSC with Industry 4.0 technologies within this emerging area of research. Design/methodology/approach: An SLR is carried out on how Lean can be integrated with Supply Chain 4.0. Using the search strings of "Lean Supply Chain 4.0," "Lean Supply Chain Management 4.0" and "Lean Supply Chain Digitalisation," a review of published literature was carried out via searches on academic databases. Findings: Industry 4.0 has a synergistic effect on the LSC and, depending on the technology and sector applied in, can complement and enhance the LSC. Similarly, the LSC is a precursor for digitalisation. There are considerable implications in the LSC 4.0 for green and sustainable processes. Practical implications: Organisations can use this study to understand what the LSC 4.0 means to industry, the benefits and motivating factors for implementation, the critical success factors (CSFs) to implementation and the challenges for implementation. Originality/value: This study adds to state of the art around the LSC 4.0 and future directions in this nascent research area. This study will aid organisations in understanding how Lean, supply chain management and Industry 4.0 can be integrated. [ABSTRACT FROM AUTHOR]

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To solve some problems of terrain mapping, it is necessary to sensory probe the territory itself and the objects located on it. For this, manipulators with force sensors installed on them are used to obtain the necessary information. The accuracy and reliability of force sensor measurements depends on the method of attaching the sensor to the manipulator and the shape of the surface of interaction of the sensor with external objects. The article describes experiments on choosing the shape of an additional surface contact for the Force Sensitive Resistor 0.5 sensor. The best mounting option turned out to be one with polyethylene foam attached to the sensor on both sides.

Recent advancements in deep learning have significantly improved the performance of remote sensing scene classification, a critical task in remote sensing applications. This study presents a new aerial scene classification model, the Multi-Scale Swin–CNN Aerial Classifier (MSCAC), which employs the Swin Transformer, an advanced architecture that has demonstrated exceptional performance in a range of computer vision applications. The Swin Transformer leverages shifted window mechanisms to efficiently model long-range dependencies and local features in images, making it particularly suitable for the complex and varied textures in aerial imagery. The model is designed to capture intricate spatial hierarchies and diverse scene characteristics at multiple scales. A framework is developed that integrates the Swin Transformer with a multi-scale strategy, enabling the extraction of robust features from aerial images of different resolutions and contexts. This approach allows the model to effectively learn from both global structures and fine-grained details, which is crucial for accurate scene classification. The model’s performance is evaluated on several benchmark datasets, including UC-Merced, WHU-RS19, RSSCN7, and AID, where it demonstrates a superior or comparable accuracy to state-of-the-art models. The MSCAC model’s adaptability to varying amounts of training data and its ability to improve with increased data make it a promising tool for real-world remote sensing applications. This study highlights the potential of integrating advanced deep-learning architectures like the Swin Transformer into aerial scene classification, paving the way for more sophisticated and accurate remote sensing systems. The findings suggest that the proposed model has significant potential for various remote sensing applications, including land cover mapping, urban planning, and environmental monitoring.

Towards the end of World War II, from January 1945, British/Canadian forces supplemented the use of topographical maps by compiling innovative specialist thematic maps to assist troops advancing eastwards from Belgium and the Netherlands into north-west Germany. ‘Soil’ maps were used to predict (1) cross-country trafficability and (2) potential airfield construction sites. Intensive bombardment of ground that was unexpectedly waterlogged as well as deliberate flooding by the enemy created mud which initially slowed cross-country movement. So too did determined German military resistance: the campaign included arguably the bloodiest operation for troops of 21st Army Group since their battle for Normandy in the summer of 1944. Significant maps, mostly at 1:100,000, are now preserved in England within the Shotton Archive at the Lapworth Museum of Geology, Birmingham. Together with expertise from geologists Major F.W. Shotton and Squadron Leader J.F. Kirkaldy, the maps contributed to terrain analysis that assisted with operational planning. [ABSTRACT FROM AUTHOR]

In disaster scenarios, up-to-date information about the affected areas is of fundamental importance, especially for Search and Rescue (SaR) teams. Although the usual space-based optical remote sensing systems provide an overview, the ground resolution of several dozen centimeters per pixel does not reveal many important details. In addition, clouds can delay the recording of the affected area for days. The approach presented here intends to address these problems. SaR teams are enabled to independently map the terrain assigned to them immediately upon arrival. These rapid maps are made easily accessible to all organizations and teams involved in the operation without delay. For the 2023 earthquake mission in Turkey, a SaR team accredited by the United Nations (UN) was equipped with a specific aerial camera and a vertical take-off and landing fixed wing drone. This system is capable of recording almost 7 km2 per hour at a ground resolution of 3 cm per pixel. The maps were made available worldwide within seconds via radio and internet connection during the flight using a web map service and visualized in the INSARAG ICMS dashboard. In addition to operational aspects, the process chain from the acquisition of the images to the publication of the rapid maps on various geographic information systems (GIS) is described. Additional benefits, the required infrastructure and the need to integrate this capability into the certified SaR teams are discussed. [ABSTRACT FROM AUTHOR]

Organizational commitment and transformational leadership play an important role in organizational success. This study explores the interrelationships between organizational commitment and employee engagement, highlighting their interconnected nature and implications for organizational effectiveness. A conceptual framework approach is used to map the interdependencies between transformational leaders, organizational commitment, and employees. Key findings and models from the selected studies are extracted to identify common themes and gaps. The conceptual framework aims to provide a foundational reference for future empirical studies and will assist practitioners in devising leadership strategies that effectively harness the potential of their workforce. The framework provides a comprehensive understanding of how leadership behaviors influence employees' emotional attachment to the organization and their subsequent engagement in their work roles. [ABSTRACT FROM AUTHOR]

Over the past two decades, the landscape of research on mathematics teacher educators (MTEs) has grown considerably. One particular area of interest has focused on MTE knowledge and the ways in which it is developed and used in teaching practice. However, studies have conceptualized MTE knowledge in different ways and have employed considerably different methodologies and approaches to its study. In an effort to understand this varied landscape, we conducted an extensive review of research on the nature and development of MTE knowledge. This review provides a broad descriptive analysis of the existing theoretical and empirical research on MTEs' knowledge, explores the theoretical underpinnings of the existing frameworks for and studies on MTE knowledge, and considers implications for future research. [ABSTRACT FROM AUTHOR]

Drama is a mode of storytelling that has transcended from traditional to online media. This study aimed to determine salient topics and trends in drama research through data mining of peer-reviewed English articles in Web of Science and Scopus. By employing the keywords "television drama," "online drama," "drama series," "web drama," and "soap opera," 2,148 articles were identified. Analysis revealed 25 distinct research topics: evolution, issues and analysis, impact, audience, and institutionalization. Prominent topics included "power and politics," "viewership and engagement," and "gender and sexuality," while less-published topics encompassed "elements and structure," "impact on culture," and "drama characterization." Notably, publication rates for all topics exhibited a significant increase since 1970. These findings underscore the need for continuous research updates across various categories to advance understanding of the interplay between society and dramas. The methodology employed can also guide future trend analyses in specific drama research topics. Plain language summary: This paper maps the terrain of drama research, referring to scholarly work focusing on television and online dramas through a systematic review using data mining techniques. We chose drama as our unit of analysis because we are interested in how this medium pervades and reflects daily life and society. Further, in this golden age of information and globalization, the social impact of media cannot be denied. Moreover, drama cuts across academic disciplines, making it vital in enriching our knowledge of society. Through data mining techniques, we analyzed drama research topics and trends spanning decades. Analysis revealed 25 distinct research topics: evolution, issues and analysis, impact, audience, and institutionalization. Prominent topics included "power and politics," "viewership and engagement," and "gender and sexuality," while less-published topics encompassed "elements and structure," "impact on culture," and "drama characterization." Notably, publication rates for all topics exhibited a significant increase since 1970. Through the techniques we used, we were able to offer baseline information that may be used by other scholars on drama-related areas or topics for further discussion, analysis, and engagement across various disciplines. In addition, our data mining methods may serve as a guide for other research. [ABSTRACT FROM AUTHOR]

This article argues that Valene Smith's contributions were fundamental for mapping out the terrain for what was envisioned as an 'anthropology of tourism.' As the first major collection of anthropological research on the topic of tourism, Smith's 1977 volume Hosts and Guests offered a wide geographical range of ethnographically grounded studies of tourism, charted typologies, and chronicled tourism impacts, much as one would map a 'field of ethnological study'. This article addresses the role of Smiths's volume in legitimizing what had previously been an off-the-radar realm of anthropological study. It also traces one of the many legacies of Smith's landmark volume: the blossoming of a realm of tourism studies oriented toward examining the politics, performance, and negotiation of identities (including ethnicity, gender, nationalism, and heritage) in and beyond encounters between 'hosts' and 'guests.' Finally, the article addresses Smith's 2001 revised edition of the volume, Hosts and Guests Revisited, arguing that her essays in that volume presciently identified 21st-century realms of interest for tourism scholars, including space tourism, voluntourism, terrorism and tourism, and cyberspace. [ABSTRACT FROM AUTHOR]

Winter wheat is a significant global food crop, and it is crucial to monitor its distribution for better agricultural management, land planning, and environmental sustainability. However, the distribution style of winter wheat planting fields is not consistent due to different terrain conditions. In mountainous areas, winter wheat planting units are smaller in size and fragmented in distribution compared to plain areas. Unfortunately, most crop-mapping research based on deep learning ignores the impact of topographic relief on crop distribution and struggles to handle hilly areas effectively. In this paper, we propose a cross-modal segmentation network for winter wheat mapping in complex terrain using remote-sensing multi-temporal images and DEM data. First, we propose a diverse receptive fusion (DRF) module, which applies a deformable receptive field to optical images during the feature fusion process, allowing it to match winter wheat plots of varying scales and a fixed receptive field to the DEM to extract evaluation features at a consistent scale. Second, we developed a distributed weight attention (DWA) module, which can enhance the feature intensity of winter wheat, thereby reducing the omission rate of planting areas, especially for the small-sized regions in hilly terrain. Furthermore, to demonstrate the performance of our model, we conducted extensive experiments and ablation studies on a large-scale dataset in Lanling county, Shandong province, China. Our results show that our proposed CM-Net is effective in mapping winter wheat in complex terrain. [ABSTRACT FROM AUTHOR]

Simultaneous localization and mapping (SLAM) is one of the key technologies for agricultural robots to build maps and localize in complex orchard environments and realize unmanned autonomous operations. Due to the complexity of the orchard environment, the single canopy feature and the diffuse reflection of light caused by the leaves, etc., the map construction process of the orchard environment leads to mismatch and increases the cumulative error of the map construction. Aiming at the above problems, this paper proposes a navigation map construction method for orchard environment based on the fusion of Scan Context and NDT-ICP. The method firstly searches the Ring key quickly to get the candidate frames, and scores the similarity between the candidate frames and the current frame, and effectively detects the loopbacks by two-stage searching algorithm to reduce the false matches in the map of orchard environment. Meanwhile, a point cloud alignment method based on the fusion of normal distribution transform coarse alignment and iterative nearest point exact alignment is used to reduce the cumulative error of the orchard environment map. The results show that the improved algorithm compensates the drift of the point cloud map with higher mapping accuracy, better real-time performance, lower resource utilization, higher overlap between the trajectory estimation and the real trajectory, smoother loops, and a 4% reduction in CPU occupancy. In the complex orchard environment, the root mean square error and standard deviation of the trajectories of this paper's algorithm are 0.57 m and 0.19 m, which are 68% and 83% higher than those of the loop detection algorithms in the Lightweight Ground Optimized Lidar Trajectory Measurement and Multivariate Terrain Mapping (LeGO-LOAM), respectively. Accurate map construction and low drift pose estimation can be performed. The research algorithm effectively reduces the influence of mis-matching and large cumulative error in the process of map construction in the orchard environment, meets the demand for high-precision environmental mapping in the orchard environment, and provides technical support for promoting unmanned operation in the orchard environment. [ABSTRACT FROM AUTHOR]

Rock avalanches are generally difficult to prevent and control due to their high velocities and the extensive destruction they cause. However, barrier structures constructed along the path of a rock avalanche can partially mitigate the magnitudes and consequences of such catastrophic events. We selected a rock avalanche in Nayong County, Guizhou Province, China as a case to study the effect of the location and height of a retaining wall on the dynamic characteristics of rock avalanche by using both actual terrain-based laboratory-model tests and coupled PFC3D–FLAC3D numerical simulations. Our findings demonstrate that a retaining wall can largely block a rock avalanche and its protective efficacy is significantly influenced by the integrity of the retaining wall. Coupled numerical simulation can serve as a powerful tool for analyzing the interaction between a rock avalanche and a retaining wall, facilitating precise observations of its deformation and destruction. The impact-curve characteristics of the retaining wall depend upon whether or not the rock avalanche-induced destruction is taken into account. The location of the retaining wall exerts a greater influence on the outcome compared to the height and materials of the retaining wall, while implementing a stepped retaining-wall pattern in accordance with the terrain demonstrates optimal efficacy in controlling rock avalanche. [ABSTRACT FROM AUTHOR]

This article presents a sophisticated approach to updating the finite element model of two historical arch masonry bridges located in the challenging terrain of Kalka Shimla mountain railway, using vibration testing results. To estimate the dynamic characteristics of the bridges, ambient vibration testing was carried out. Next, initial finite element models of the bridges were developed based on geometrical survey data. Sensitivity analysis was used to determine which parameters needed to be modified for the bridges. The response surface method and global optimization techniques were employed to identify the optimal values of structural parameters that would result in a satisfactory agreement between the numerical and measured natural frequencies of the heritage bridges. Ultimately, the methodology provided a surrogate mathematical model to represent the relationship between structural parameters and dynamic response, and could predict the damage status of the historical bridges. [ABSTRACT FROM AUTHOR]

Religion has been associated with human values and competencies needed to shape young people into responsible citizens able to make a positive difference in the lives of others. However, this correlation remains dependent on the context. As the correlation has not yet been investigated in Mauritius, which is a multicultural and multi-ethnic country, this study aims to bridge this knowledge gap using a purely positivist epistemological approach. It will analyze the Mauritius-specific data derived from a public data set which was part of the "Mapping the Terrain" project to examine the correlation between religion and a broad set of human values and competencies, namely forgiveness, individualistic orientation, collectivistic orientation, self-efficacy, problem-solving, meaning-making, sense of belongingness, religiosity/spirituality, hope, life satisfaction, gratitude, emotion, regulation, empathy, and self-regulation. The descriptive analysis and the Structural Equation Model (SEM), developed using the SPSS and AMOS software, revealed that (i) independent of ethnic group, educational stakeholders in Mauritius have a high affinity to most of the human values and competences, and (ii) as compared with others, the Muslim community has a slightly lower affinity to forgiveness, life satisfaction, individualistic orientation, self-efficacy, and problem solving. These data, which represent important baseline information needed to inform policymakers and other educational stakeholders, were used to derive a set of recommendations that will further the development of the key human values and competencies needed to promote harmony in a multicultural and multi-ethnic country. [ABSTRACT FROM AUTHOR]

Climate change pressure on the Arctic permafrost is rising alarmingly, creating a decisive need to produce Pan-Arctic scale permafrost landform and thaw disturbance information from remote sensing (RS) data. Very high spatial resolution (VHSR) satellite images can be utilized to detect ice-wedge polygons (IWPs)– the most important and widespread landform in the Arctic tundra region - across the Arctic without compromising spatial details. Automated analysis of peta-byte scale VHSR imagery covering millions of square kilometers is a computationally challenging task. Traditional semantic segmentation requires the use of task specific feature extraction with conventional classification techniques. Semantic complexity of VHSR images coupled with landscape heterogeneity makes it difficult to use conventional classification approaches to produce Pan-Arctic scale geospatial products. This leads to adapting deep convolutional neural network (DLCNN) approaches that have excelled in computer vision (CV) applications. Transitioning domains from everyday image understanding to remote sensing image analysis is challenging. This study aims to systematically investigate two main obstacles confronted when adapting DLCNNs in large-scale RS image analysis tasks; 1) the limited availability labeled data sets and 2) the prohibitive nature of hyperparameter tunning when designing DLCNNs that can capture the rich characteristics embedded in remotely-sensed images. With a case study on the production of the first pan-Arctic ice-wedge polygon map using thousands of VHSR images, we demonstrate the use of transfer learning and the impact of hyperparameter tuning with a 16% improvement of the Mean Average Precision (mAP50).

The rapid advancement of mobile laser scanner technology used for terrain mapping, among other things, imposes increasing requirements for scanning frequency and distance measurement accuracy. To meet these requirements, rangefinder modules are expected to operate with high echo signal dynamics and to allow accurate distance measurement even based on single-laser-pulse echo detection. Such performance can be potentially achieved using pulsed time-of-flight (ToF) laser rangefinders (LRF). In conventional ToF modules, however, the STOP signal (for time counter interruption) is generated using a straightforward fixed-threshold comparator method. Unfortunately, it corresponds to the so-called walk error, i.e., the dependence of the measured time of flight on the magnitude of the echo signal. In most ranging applications, however, the LRF detection channel can be exposed to an extremely large span of received echo power levels, which depend on the distance measured, type of target surface, atmospheric transmission, etc. Thus, the walk error is an inseparable element of the conventional ToF technique and creates a fundamental limit for its precision. This article presents a novel method of walk error compensation in real time. By using our authorial electronic circuit for measuring the magnitude of the echo signal, it is possible to effectively compensate for the walk error even when the echo signal brings the detection channel amplifiers into saturation. In addition, the paper presents a laboratory method for calibrating the walk error compensation curve. [ABSTRACT FROM AUTHOR]

The relationship between personality traits and Quality of Life (QoL) has garnered increasing scholarly attention, particularly within the context of China. This scoping review synthesizes existing literature on the connection between the Big Five personality traits (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) and QoL among the Chinese population. The review explores correlations, measurement instruments, and theoretical frameworks employed in these studies. The study's significance lies in the profound implications for healthcare policies, organizational behavior, and social welfare programs in China, where rapid social and economic changes impact well-being. Investigating personality traits' impact on QoL can inform tailored interventions benefiting diverse Chinese subpopulations. This review addresses three primary research questions: (1) How do the Big Five personality traits correlate with QoL in different Chinese subpopulations, and what are the mediating or moderating factors? (2) What instruments assess these traits and QoL in the Chinese cultural context, and how are they validated? (3) What theoretical or disciplinary frameworks guide this research in China? Following a systematic PRISMA-ScR framework and a customized "C-BFQLC" protocol, the study identified 170 relevant documents. After rigorous screening, ten studies met the inclusion criteria, constituting 5.03% of the initial records. This scoping review critically examines the interplay between the Big Five personality traits and QoL in China, a context marked by rapid socioeconomic changes and cultural diversity. Employing a systematic approach guided by the PRISMAScR framework and our unique 'C-BFQLC' protocol, we meticulously analyzed 170 documents, selecting nine that met our rigorous inclusion criteria. Despite the emerging nature of this research area in the Chinese context, our study reveals significant insights into how individual personality traits influence various dimensions of well-being. The implications of these findings are profound, extending beyond academic discourse to inform healthcare policies, organizational behavior, and social welfare programs in China. Our review not only offers a comprehensive synthesis of the current research landscape but also identifies critical gaps in the literature. It emphasizes the urgent need for further culturally nuanced research to understand the complex dynamics of personality traits and QoL in China. This work lays a foundational framework for future investigations and the development of tailored interventions aimed at enhancing the well-being of diverse Chinese populations, including the elderly, people with disabilities, and specific occupational groups. [ABSTRACT FROM AUTHOR]

The present research focuses on studying the river Chenab morphology upstream of the Panjnad (PJD) Barrage, which has altered Chenab's flow, causing erosion on the Reduced Distance (RD) 55+000 to 35+000 along the Right Marginal Bund (RMB). A field survey was conducted to understand river morphology focused on sedimentation, erosion, and island formation. An 18 km study Upstream (U/S) along Chenab and Sutlej utilized plane table survey methods with tools like levelling tools, compasses, and plane table boards. Mapping terrain between the Right Guide Bank (RGB), Left Guide Bank (LGB), RMB, and Left Marginal Bund (LMB) was the goal. A detailed drawing of the river's topography from the main weir was made due to Chenab's low flow creating islands near bay number 13-50. The studied river section, RD: 35+000 to 55+000, shows braiding, unstable sandbars, and monsoon-driven sediment influx, impacting canal diversion and morphology. Erosion from RD 35+000 to 55+000 on the Chenab's right bank persists despite prior efforts using j-head spurs, sloping spurs, and mole head spurs. Sutlej's floods damage areas along LMB up to J-Head Spur RD 5+500 LMB. Concentrated discharge threatens the upper curved part of the RGB. (U/S island formation impairs PJD's discharge by blocking the Annexe weir. A permanent island divides Chenab streams from RD 36+000 to the confluence, threatening infrastructure along RMB (RD 36+000 to 50+000). The project benefits both industry and society by enhancing irrigation reliability, and water supply and reducing flood risks in nearby areas. [ABSTRACT FROM AUTHOR]

In order to resolve redundancy and path planning of a high DOF mobile manipulator using conventional approaches like Jacobian and a pseudoinverse method, researchers face the limitation of computational load and delay in response. If such kind of mobile manipulator is traversing the rough terrain, then conventional methods become too costly to implement due to the handling of redundant joints, obstacles, and wheel-terrain interaction. A few optimization-based redundancy resolution approaches try incorporating wheel-terrain interaction but fail in real-time response. This paper describes a 14 DOF Rover Manipulator System's end-effector path-tracking approach using CG-Space framework to incorporate wheel-terrain interaction. CG-Space means the center of gravity (CG) locus of the Rover. The Rover's CG is calculated while traversing over 3D terrain using a multivariable optimization method and a 3D point cloud image of the actual terrain and stored as CG-Space over the given terrain. First of all, we decide which part of the system moves to track the path, that is, arm or Rover, depending upon the manipulator's work volume and manipulability measure restrictions. The next task is to obtain the Rover pose according to the end-effector path using a simple arm's inverse kinematic solution between the CG-Space and end-effector task space without resolving redundancy. Meanwhile, obstacles and non-traversable regions are avoided in CG-Space. On diverse 3D terrains, simulation and experimental results support the suggested CG-Space framework for end-effector tracking. [ABSTRACT FROM AUTHOR]

South China is dominated by mountainous agriculture and croplands that are at risk of flood disasters, posing a great threat to food security. Synthetic aperture radar (SAR) has the advantage of being all-weather, with the ability to penetrate clouds and monitor cropland inundation information. However, SAR data may be interfered with by noise, i.e., radar shadows and permanent water bodies. Existing cropland data derived from open-access landcover data are not accurate enough to mask out these noises mainly due to insufficient spatial resolution. This study proposed a method that extracted cropland inundation with a high spatial resolution cropland mask. First, the Proportional–Integral–Derivative Network (PIDNet) was applied to the sub-meter-level imagery to identify cropland areas. Then, Sentinel-1 dual-polarized water index (SDWI) and change detection (CD) were used to identify flood area from open water bodies. A case study was conducted in Fujian province, China, which endured several heavy rainfalls in summer 2022. The result of the Intersection over Union (IoU) of the extracted cropland data reached 89.38%, and the F1-score of cropland inundation achieved 82.35%. The proposed method provides support for agricultural disaster assessment and disaster emergency monitoring. [ABSTRACT FROM AUTHOR]

This research presents a comprehensive analysis of the production of terrain passability maps in southeastern Serbia, employing a multi-criteria decision-making (MCDM) analysis. The study integrates various geographical and infrastructural aspects, assigning coefficients to each input parameter, including rivers, roads, rails, CORINE Land Cover (CLC), soil, slope, and the Topographic Ruggedness Index (TRI). The introduction of the TRI marks an innovative advancement in terrain analysis and passability. By comparing wet and dry periods, the study provides critical insights into the dynamic nature of terrain passability, with implications for transportation planning and emergency response. The research's innovative approach and detailed examination set it apart, offering valuable contributions to scholarly comprehension and practical applications. The findings underscore the potential for interdisciplinary collaboration and the broad impact of geographic information systems (GIS) and terrain analysis in addressing real-world challenges. Future research may explore additional factors influencing terrain passability and expand the geographical scope of the study. [ABSTRACT FROM AUTHOR]

This article focuses on higher education access for undocumented immigrants in the United States. Since individual states develop and govern their own policies, the political landscape around college access is always in a state of flux. This is confusing to school counselors, families, and students. We use cartography to make sense of this increasingly complex policy terrain. In addition to displaying a state-by-state overview of access and funding options for undocumented students, we interrogate the (un)intended consequences of these policies and reveal "sites for change and activism" (Marx, 2023, p. 286). [ABSTRACT FROM AUTHOR]

Accurate terrain mapping information is very important for foot landing planning and motion control in foot robots. Therefore, a terrain mapping method suitable for an indoor structured environment is proposed in this paper. Firstly, by constructing a terrain mapping framework and adding the estimation of the robot's pose, the algorithm converts the distance sensor measurement results into terrain height information and maps them into the voxel grid, and effectively reducing the influence of pose uncertainty in a robot system. Secondly, the height information mapped into the voxel grid is downsampled to reduce information redundancy. Finally, a preemptive random sample consistency (preemptive RANSAC) algorithm is used to divide the plane from the height information of the environment and merge the voxel grid in the extracted plane to realize the adaptive resolution 2D voxel terrain mapping (ARVTM) in the structured environment. Experiments show that the proposed mapping algorithm reduces the error of terrain mapping by 62.7% and increases the speed of terrain mapping by 25.1%. The algorithm can effectively identify and extract plane features in a structured environment, reducing the complexity of terrain mapping information, and improving the speed of terrain mapping. [ABSTRACT FROM AUTHOR]

Unmanned ground vehicles (UGVs) have emerged as a promising solution for reconnaissance missions, overcoming labor cost, frequency, and subjectivity issues associated with manual procedures. However, for dynamic environments such as construction sites, the constantly changing conditions hinder a manager from planning the UGV's paths. For an autonomous monitoring mission, the path planning should be dealt with by having a map with the site's most recent scene. In this study, we develop a mobile app capable of local map updates by overlaying an image on a mapping API (e.g., Google Maps) hence working as a digital twin capable of creating a dynamic representation of the updated terrain over the mapping API. UGV operators can draw a path on such an updated construction scene using a tablet PC or smartphone. Discrete GPS information (e.g., latitudinal, and longitudinal) is executed for the UGV's controller. In the overlaying procedure, the homographic relation between the image and map is automatically computed and then projected. We successfully demonstrated the capabilities of the technique with two construction sites and a soccer field using images from the ground and satellite, respectively. The error generation is quantitatively measured and analyzed. [ABSTRACT FROM AUTHOR]

The development of wheeled ground robots has enabled them to be used for a variety of tasks. These robots must be able to move with accuracy and precision, especially when faced with obstacles or inclines. To improve the movement of these robots on a slope, lidar data can be used to detect the location and shape of obstacles. In recent years, Lidar technology has become an essential tool for various robotic applications. It has proven to be a game-changer in the field of autonomous navigation, especially in situations where robots have to operate in unknown environments. Lidar technology provides a high-resolution 3D map of the environment around the robot, enabling it to navigate autonomously while avoiding obstacles. In this paper, we discuss the use of Lidar technology in improving the movement of a wheeled ground robot on a slope. We describe the steps involved in obtaining Lidar data, processing the data to create a 3D map of the environment, and using the map to plan more efficient movement of the robot. We present an applied example of how Lidar data improves the movement of a ground robot with wheels on a slope, calculating the inclination of the ground, calculating the force required for the movement of the robot, creating three-dimensional models of the terrain to be navigated, creating plans for more efficient movement, and reducing damage and wear of the robot.

Intertidal mudflats are an important component of the coastal geomorphological system at the interface between ocean and land. Accurate and up-to-date mapping of intertidal topography at high spatial resolution, and tracking of its changes over time, are essential for coastal habitat protection, sustainable management and vulnerability analysis. Compared with ground-based or airborne terrain mapping, the satellite-based waterline method is more cost-effective for constructing large-scale intertidal topography. However, the accuracy of the waterline method is affected by the extraction of waterlines and the calibration of waterline height. The blurred boundary between turbid water and mudflats in the tide-dominated estuary brings enormous challenges in accurate waterline extraction, and the errors in estuarine water level simulations prevent the direct calibration of waterline heights. To address these issues, this paper developed a novel deep learning method using a parallel self-attention mechanism and boundary-focused hybrid loss to extract turbid estuarine waterlines accurately from dense Sentinel-2 time series. UAV photogrammetric surveys were employed to calibrate waterline heights rather than the simulated water levels, such that the error propagation is constrained effectively. Annual intertidal topographic maps of the Yangtze estuary in China were generated from 2020 to 2022 using the optimized waterline method. Experimental results demonstrate that the proposed deep learning method could achieve excellent performance in land and water segmentation in time-varying tidal environments, with better generalization capability compared with benchmark U-Net, U-Net++ and U-Net+++ models. The comparison between the generated topography and UAV photogrammetric observations resulted in an RMSE of 13 cm, indicating the effectiveness of the optimized waterline method in monitoring morphological changes in estuarine mudflats. The generated topographic maps successfully identified hotspots of mudflat erosion and deposition. Specifically, the mudflats connected to the land predominantly experienced deposition of 10–20 cm over the two-year period, whereas the offshore sandbars exhibited instability and significant erosion of 20–60 cm during the same period. These topographic maps serve as valuable datasets for providing scientific baseline information to support coastal management decisions. [ABSTRACT FROM AUTHOR]

The location of Low-Altitude Flight Service Station (LAFSS) is a comprehensive decision work, and it is also a multi-objective optimization problem (MOOP) with constraints. As a swarm intelligence search algorithm for solving constrained MOOP, the Immune Algorithm (IA) retains the excellent characteristics of genetic algorithm. Using some characteristic information or knowledge of the problem selectively and purposefully, the degradation phenomenon in the optimization process can be suppressed and the global optimum can be achieved. However, due to the large range involved in the low-altitude transition flight, the geographical characteristics, economic level and service requirements among the candidate stations in the corridor are quite different, and the operational safety and service efficiency are interrelated and conflict with each other. And all objectives cannot be optimal. Therefore, this article proposes a Modified Immune Algorithm (MIA) with two-layer response to solve the constrained multi-objective location mathematical model of LAFSS. The first layer uses the demand track as the cell membrane positioning pattern recognition service response distance to trigger the innate immunity to achieve the basic requirements of security service coverage. In the second layer, the expansion and upgrading of adjacent candidate sites are compared to the pathogen's effector, and the adaptive immunity is directly or indirectly triggered again through the cloning, mutation and reproduction between candidate sites to realize the multiobjective equilibrium of the scheme. Taking 486,000 km2 of Sichuan Province as an example, MIA for LAFSS is simulated by the MATLAB platform. Based on the Spring open source application framework of Java platform, the cesiumjs map data is called through easyui, and the visualization of site selection scheme is presented with the terrain data of Map World as the background. The experimental results show that, compared with dynamic programming and ordinary immunization, the immune trigger mode of double response and the improved algorithm of operation parameter combination designed by the Taguchi experiment, the total economic cost of location selection is reduced by 26.4%, the service response time is reduced by 25%, the repeat coverage rate is reduced by 29.5% and the effective service area is increased by 17.5%. The security risk, service efficiency and location cost are balanced. The present work is to provide an effective location method for the layout number and location of local transfer flight service stations. For complex scenes with larger scale of low-altitude flight supply and demand and larger terrain changes in the region, the above research methods can be used to effectively split and reduce the dimension. [ABSTRACT FROM AUTHOR]

Hebrus Valles is an outflow channel system in the plain-forming terrains of southeastern Utopia Planitia, Mars. These terrains may have formed through a combination of liquid water and volcanic processes, yet their nature, subsurface structure, and composition remain unclear. We investigate these terrains by mapping subsurface reflectors across 540 Shallow Radar (SHARAD) profiles and applying two complementary loss tangent inversion techniques. We find moderate loss tangent values across some subregions of Granicus Valles and Hyblaeus Fossae (tan δ = 0.0162 ± 0.0004 and tan δ = 0.019 ± 0.002, respectively), suggesting the presence of basaltic lava flows. We interpret non-detections in the other flows in Granicus Valles to be due to the presence of radar-lossy materials formed through aqueous processes, which supports the hypothesized occurrence of lahars in this region. A small area near Hebrus Valles exhibits subsurface reflectors with low to moderate loss tangents (tan δ = 0.010 ± 0.003), suggesting the presence of pristine lava flows or sedimentary materials capped by lava flows. We also find a widespread occurrence of very low-loss tangent materials near Hyblaeus Dorsa (tan δ = 0.0045 ± 0.0002), which may represent a lobe of the Medusae Fossae Formation or similar high-porosity materials buried underneath a lava flow. Together, these findings suggest that volcanic activity played a central role in the formation of terrains across the broader Hebrus Valles region. [ABSTRACT FROM AUTHOR]

Illicit maritime activity, such as piracy and smuggling, is a global issue that often occurs in areas where interdiction authorities are sparse, ground-based monitoring technologies like radar lack range and coverage, and dark vessels abound. Our objective was to assess vessel congregation patterns, identify likely beaching areas based on terrain maps, and use them to narrow the search for potential smuggling transfer and overland routes in a specific region along the Puntland coast of Somalia. To accomplish this goal, we developed automated protocols applied to WorldView satellite imagery for (1) vessel detection and size classification, (2) shallow-water bathymetric characterization, and (3) coastal topography mapping. Utilizing a single sensor for vessel detection and topographic and bathymetric extraction at high spatial resolution and at a high re-visit rate provides a simplification to near-shore characterization for monitoring purposes. The extracted topography and bathymetry are then presented as a single, comprehensive perspective of a coastal region. The vessel-detection algorithm identified all vessels larger than approximately 15 metres in length (35 of 35), but misidentified six artefacts (i.e. false positives), resulting in an overall accuracy of 85%. The combined vessel and terrain maps facilitated the identification of a potential beaching and overland transportation route location. [ABSTRACT FROM AUTHOR]

Aiming at the problems that the current 2D monitoring system for trailing suction hopper dredger (TSHD) construction lacks intuition, and the installation, deployment and maintenance of the client is more troublesome, a set of 3D monitoring system for TSHD construction based on B/S architecture was designed. The system adopts WebGL technology, by solving key technical problems such as digital terrain model construction, terrain reconstruction and data compression, the real- time 3D dynamic update of the underwater mud surface during the dredging process of the TSHD was realized, and according to the real-time acquired sensor data of the THSD, a 3D dynamic display of the current hull operation status and the attitude of the rake arm was carried out, so as to help dredging personnel to intuitively understand the water depth of the construction area and dredging trajectory, which has important practical significance for improving the flatness of the construction area and controlling the construction quality of the area. [ABSTRACT FROM AUTHOR]

An optimal path provides efficient operation of unmanned ground vehicles (UGVs) for many kinds of tasks such as transportation, exploration, surveillance, and search and rescue in unstructured areas that include various unexpected obstacles. Various onboard sensors such as LiDAR, radar, sonar, and cameras are used to detect obstacles around the UGVs. However, their range of view is often limited by movable obstacles or barriers, resulting in inefficient path generation. Here, we present the aerial online mapping system to generate an efficient path for a UGV on a two‐dimensional map. The map is updated by projecting obstacles detected in the aerial images taken by an unmanned aerial vehicle through an object detector based on a conventional convolutional neural network. The proposed system is implemented in real‐time by a skid steering ground vehicle and a quadcopter with relatively small, low‐cost embedded systems. The frameworks and each module of the systems are given in detail to evaluate the performance. The system is also demonstrated in unstructured outdoor environments such as in a football field and a park with unreliable communication links. The results show that the aerial online mapping is effective in path generation for autonomous UGVs in real environments. [ABSTRACT FROM AUTHOR]

In recent years, remote sensing images of various types have found widespread applications in resource exploration, environmental protection, and land cover classification. However, relying solely on a single optical or synthetic aperture radar (SAR) image as the data source for land cover classification studies may not suffice to achieve the desired accuracy in ground information monitoring. One widely employed neural network for remote sensing image land cover classification is the U-Net network, which is a classical semantic segmentation network. Nonetheless, the U-Net network has limitations such as poor classification accuracy, misclassification and omission of small-area terrains, and a large number of network parameters. To address these challenges, this research paper proposes an improved approach that combines both optical and SAR images in bands for land cover classification and enhances the U-Net network. The approach incorporates several modifications to the network architecture. Firstly, the encoder-decoder framework serves as the backbone terrain-extraction network. Additionally, a convolutional block attention mechanism is introduced in the terrain extraction stage. Instead of pooling layers, convolutions with a step size of 2 are utilized, and the Leaky ReLU function is employed as the network's activation function. This design offers several advantages: it enhances the network's ability to capture terrain characteristics from both spatial and channel dimensions, resolves the loss of terrain map information while reducing network parameters, and ensures non-zero gradients during the training process. The effectiveness of the proposed method is evaluated through land cover classification experiments conducted on optical, SAR, and combined optical and SAR datasets. The results demonstrate that our method achieves classification accuracies of 0.8905, 0.8609, and 0.908 on the three datasets, respectively, with corresponding mIoU values of 0.8104, 0.7804, and 0.8667. Compared to the traditional U-Net network, our method exhibits improvements in both classification accuracy and mIoU to a certain extent. [ABSTRACT FROM AUTHOR]

This paper documents substantial improvements to the original automated avalanche terrain exposure mapping (AutoATES v1.0) algorithm. The most significant drawbacks of AutoATES v1.0 have been addressed by including forest density data, improving the avalanche runout estimations in low-angle runout zones, accounting for overhead exposure and open-source software. The algorithm also supports the new ATES v2.0 terrain class 'extreme' terrain. We used two benchmark maps from Bow Summit and Connaught Creek to validate the improvements from AutoATES v1.0 to v2.0. For Bow Summit, the F1 score (a measure of how well the algorithm performs) improved from 64.01% to 77.30%. For Connaught Creek, the F1 score improved from 39.81% to 71.38%. The main challenge limiting large-scale mapping is the determination of optimal input parameters for different regions and climates. In areas where AutoATES v2.0 is applied, it can be a valuable tool for avalanche risk assessment and decision. Ultimately, our goal is for AutoATES v2.0 to enable efficient, large-scale, and potentially global ATES mapping in a standardized manner rather than based solely on expert judgement. [ABSTRACT FROM AUTHOR]

The regional geochemical mapping was carried out in the Indo-Gangetic alluvial plain of the Western and Central India which was previously classified as Varanasi alluvium, a polycyclic sequence of large alluvial fans transported by perennial Himalayan Rivers. The results based on the geochemical dispersion pattern of major and minor oxides as well as trace elements conclude that there are four major factors determining the dispersion patterns of the variables which are: (1) the underlying lithology with a strike of NE-SW, (2) transport of sediments along the general slope of the study area towards south and south-west as reflected in the Digital Elevation Model, (3) formation of Reh and (4) anthropogenic influence. The present study focusing on the effect of the underlying geology on the geochemical signature of the study area infers that the geochemical distribution pattern of certain variables clearly follows the trend of the Delhi fold belt. It can be interpreted that the subsurface lithology belonging to Delhi Supergroup is present at a depth of 500m which is considerably shallower, imprinting its geochemical signature on the sediments. The presence of minor outcrops of quartzite belonging to the Delhi Supergroup with the NE-SW strike is also documented in the study area. Based on this study we propose to reclassify the Varanasi Alluvium sediments as equivalent to Banda Alluvium. The study also demonstrates the utility of geochemical mapping as a tool to map the subsurface geology of an alluvial terrain. The results of this study are also corroborated by the regional scale gravity survey. [ABSTRACT FROM AUTHOR]

The geologic cross-section is an indispensable complement to the geologic map and is the model that represents the underground extension of the materials of the structures. It still lacks an automatic way to adapt to digital transformation. A novel method is proposed to improve the efficiency and accuracy of geologic cross-section construction by spatial data projection. The method has three main steps as follows: 1) Calculation. All key components on the cross-section, including topographic profile and structural data, are automatically generated through a relational database table. 2) Spatial coordinates projection. A cross-section in a free direction comes from the projection of a three dimensional data source. The geometric parameters of the geologic cross-section are obtained through the dimensionality reduction projection of the point coordinates. 3) Data extension. The extension of geologic structures in the subsurface and denuded areas and the spatial relationship between contact lines and topographic profiles are discussed. Four case studies, including Digital Contour Map, Remote Sensing, Global Navigator Satellite System (GNSS), and Laser rangefinder, successfully demonstrate that the proposed method is a simple, flexible, fast, precise, and visual process that can generate digital cross-sections automatically and efficiently through multi-source topographic and geologic data. [ABSTRACT FROM AUTHOR]

Unmanned surface vehicles (USVs) have wide applications in marine inspection and monitoring, terrain mapping, and water surface cleaning. Accurate and robust environment perception ability is essential for achieving autonomy in USVs. Small object detection on water surfaces is an important environment perception task, typically achieved by visual detection using cameras. However, existing vision-based small object detection methods suffer from performance degradation in complex water surface environments. Therefore, in this paper, we propose a millimeter-wave (mmWave) radar-aided vision detection method that enables automatic data association and fusion between mmWave radar point clouds and images. Through testing on real-world data, the proposed method demonstrates significant performance improvement over vision-based object detection methods without introducing more computational costs, making it suitable for real-time application on USVs. Furthermore, the image–radar data association model in the proposed method can serve as a plug-and-play module for other object detection methods. [ABSTRACT FROM AUTHOR]

Navigation over torturous terrain such as those in natural subterranean environments presents a significant challenge to field robots. The diversity of hazards, from large boulders to muddy or even partially submerged Earth, eludes complete definition. The challenge is amplified if the presence and nature of these hazards must be shared among multiple agents that are operating in the same space. Furthermore, highly efficient mapping and robust navigation solutions are absolutely critical to operations such as semi-autonomous search and rescue. We propose an efficient and modular framework for semantic grid mapping of subterranean environments. Our approach encodes occupancy and traversability information, as well as the presence of stairways, into a grid map that is distributed amongst a robot fleet despite bandwidth constraints. We demonstrate that the mapping method enables safe and enduring exploration of subterranean environments. The performance of the system is showcased in high-fidelity simulations, physical experiments, and Team MARBLE’s entry in the DARPA Subterranean Challenge which received third place.