Map Representation Research Articles

A Cartographic Perspective on the Planetary Geologic Mapping Investigation of Ceres

The NASA Dawn spacecraft visited asteroid 4 Vesta between 2011 and 2012 and dwarf planet 1 Ceres between 2015 and 2018 to investigate their surfaces through optical and hyperspectral imaging and their composition through gamma-ray and neutron spectroscopy. For the global mapping investigation of both proto-planets, geologic mappers employed Geographic Information System (GIS) software to map 15 quadrangles using optical and hyperspectral data and to produce views of the geologic evolution through individual maps and research papers. While geologic mapping was the core motivation of the mapping investigation, the project never aimed to produce homogeneous and consistent map representations. The chosen mapping approach and its implementation led to a number of inconsistencies regarding cartographic representation, including differential generalization through varying mapping scales, topologic inconsistencies, lack of semantic integrity, and scale consistency, and ultimately, to the management of reusable research data. Ongoing data acquisition during the mapping phase created additional challenges for the homogenization of mapping results and a potential derivation of a global map. This contribution reviews cartographic and data perspectives on the mapping investigation of Ceres and highlights (a) data sources, (b) the cartographic concept, (c) mapping conduct, and (d) dissemination as well as research-data management arrangements. It furthermore discusses decisions and experiences made during mapping and finishes with a set of recommendations from the viewpoint of the cartographic sciences.

Resilient digital twin modeling: A transferable approach

High-fidelity digital twin models include not only concrete geometric models but also intrinsic models such as mechanism models and data models, which require high information dimensionality. Consequently, modeling, validation, and implementation cycles become challenging and time-consuming. The reuse of existing digital twin models to achieve rapid transference and utilization of models is becoming one of the hot topics in digital twin modeling. To this effect, aiming at how to reuse the multi-dimensional information of existing models to improve modeling efficiency and resilience, this paper proposes a rapid transferable modeling approach for digital twin models. The approach proposes a unified mapping and digital representation of physical entities based on information metamodels, which can rapidly instantiate digital twin assets. Then, the transferable approaches from the geometry dimension, behavior dimension, and algorithm dimension are given respectively, which include the information model reuse, action feature query and rule generation, as well as fine-tuning of data-driven mechanisms and domain adaptive of digital twins. Finally, through a practical case study, the result shows that the efficiency of modeling improves by 60.39%, and the effectiveness of the model after domain adaptation improves by 6.94%.

Unmanned aerial vehicle orthogonal laser localization by Gaussian mixture model‐based map representation

AbstractLocalization is a core problem in mobile robot navigation. Simultaneous localization and mapping (SLAM) costs much for an unmanned aerial vehicle (UAV). This research aims to design an orthogonal laser scan device for localization and to save computation costs. Based on disturbance analysis, residual influences on sensor state are quantitative, and they are related to uncertainty and sensitivity. This research applied the residual selection method to a UAV. The feature point detection utilises multi‐scale and Gaussian model fitting techniques to guarantee true positives. The map is represented by Gaussian Mixture Models (GMM) with lower memory costs. The orthogonal laser scan device is composed and placed on a UAV for real‐time three‐dimensional localization, whose errors are at the centimeter level.

The use of budget UAV systems and GIS spatial analysis in cadastral and construction surveying for building planning

The uncertainty that comes with planning, constructing, and maintaining buildings is a constant issue for architects and civil engineers. As topography is the framework that unites architecture and landscape, the design and planning projects heavily rely on a range of monitoring, surveying methods and comprehensive field data. Along with the traditional topo-geodetic instrumentation used in land and construction surveying, unmanned aerial vehicles equipped with digital cameras and structure from motion software have been increasingly used recently in a variety of fields to create high-resolution digital elevation models. Despite this widespread use, in the majority of surveying projects it is considered that the topographic representations produced through this technology is inferior to that obtained with surveys conducted using conventional methods, along with other constraints imposed by legislation, environment and weather conditions. While certain limitations of unmanned aerial vehicle (UAV) systems are challenging, their advantage for gathering data from a different perspective and the generated outputs have the potential to significantly advance the construction industry. The present article provides an overview of the usefulness of budget UAV systems in developing a methodology that accompanies the conventional survey process for civil engineering applications. Thus, along with the established survey for cadastral and technical documentations necessary for the architectural process, a complementary UAV survey was developed, with subsequent spatial analysis in a geographic information system (GIS), in order to expand the array of deliverables. These include useful orthophoto map, larger-scale and denser representations of the topography, digital surface and terrain models, slope, aspect and solar radiation maps which will offer helpful information and instructions at the start of the construction planning process. The methodology contains two case studies with different degrees of terrain and vegetation challenges, and also presents an accuracy assessment and overall benefits discussion regarding the UAV implementation.

Mapping of Potential Fuel Regions Using Uncrewed Aerial Vehicles for Wildfire Prevention

This paper presents a comprehensive forest mapping system using a customized drone payload equipped with Light Detection and Ranging (LiDAR), cameras, a Global Navigation Satellite System (GNSS), and Inertial Measurement Unit (IMU) sensors. The goal is to develop an efficient solution for collecting accurate forest data in dynamic environments and to highlight potential wildfire regions of interest to support precise forest management and conservation on the ground. Our paper provides a detailed description of the hardware and software components of the system, covering sensor synchronization, data acquisition, and processing. The overall system implements simultaneous localization and mapping (SLAM) techniques, particularly Fast LiDAR Inertial Odometry with Scan Context (FASTLIO-SC), and LiDAR Inertial Odometry Smoothing and Mapping (LIOSAM), for accurate odometry estimation and map generation. We also integrate a fuel mapping representation based on one of the models, used by the United States Secretary of Agriculture (USDA) to classify fire behavior, into the system using semantic segmentation, LiDAR camera registration, and odometry as inputs. Real-time representation of fuel properties is achieved through a lightweight map data structure at 4 Hz. The research results demonstrate the effectiveness and reliability of the proposed system and show that it can provide accurate forest data collection, accurate pose estimation, and comprehensive fuel mapping with precision values for the main segmented classes above 85%. Qualitative evaluations suggest the system’s capabilities and highlight its potential to improve forest management and conservation efforts. In summary, this study presents a versatile forest mapping system that provides accurate forest data for effective management.

On functional reproducing kernels

Abstract We show that even if a Hilbert space does not admit a reproducing kernel, there could still be a kernel function that realizes the Riesz representation map. Constructions in spaces that are the Fourier transform of weighted L 2 {L}^{2} spaces are given. With a mild assumption on the weight function, we are able to reproduce Riesz representatives of all functionals through a limit procedure from computable integrals over compact sets, despite that the kernel is not necessarily in the underlying Hilbert space. Distributional kernels are also discussed.

Clustering eye-movement data uncovers students’ strategies for coordinating equations and diagrams of vector fields

AbstractIn mathematics education, students are repeatedly confronted with the tasks of interpreting and relating different representations. In particular, switching between equations and diagrams plays a major role in learning mathematical procedures and solving mathematical problems. In this article, we investigate a rather unexplored topic with precisely such requirements—that is, vector fields. In our study, we first presented a series of multiple-choice tasks to 147 introductory university students at the beginning of their studies and recorded students’ eye movements while they matched vector field diagrams and equations. Thereafter, students had to solve a similar coordination task on paper and justify their reasoning. Two cluster analyses were performed including (i) transition and fixation data on diagrams and options (Model 1), and (ii) additionally the number of horizontal and vertical saccades on the diagram (Model 2). In both models, two clusters emerge—with Model 1 distinguishing behaviors related to representational mapping and Model 2 additionally differentiating students according to representation-specific demands. Model 2 leads to a better distinction between the groups in terms of different performance indicators (test score, response confidence, and spatial ability) which also transfers to another task format. We conclude that vertical and horizontal saccades reflect executive actions of perception when approaching vector field coordination tasks. Thus, we recommend targeted interventions for mathematics lessons; these lessons must focus on a visual handling of the vector field diagram. Further, we infer that students’ difficulties can be attributed to covariational reasoning, thereby indicating the need for further investigations. From a methodological perspective, we reflect on the triangulation of eye-tracking and verbal data in (multiple-choice) assessment scenarios.

A Multirobot System for 3-D Surface Reconstruction With Centralized and Distributed Architectures

In this article, we propose an original solution to the problem of surface reconstruction of large-scale unknown environments, with multiple cooperative robots. As they progress through the 3-D environment, the robots rely on volumetric maps obtained via a TSDF representation to extract discrete incomplete surface elements (ISEs), and a list of candidate viewpoints is generated to cover them. A next-best-view planning approach, which approximately solves a traveling salesman problem (TSP) via greedy allocation, is then used to iteratively assign these viewpoints to the robots. Two multiagent architectures, a centralized one (TSP-Greedy Allocation or TSGA) and a distributed one (dist-TSGA), in which the robots locally compute their maps and share them, are developed and compared. Extensive numerical and real-world experiments with multiple aerial and ground robots in challenging 3-D environments show the flexibility and effectiveness of our surface representation of a volumetric map. The experiments also shed light on the nexus between reconstruction accuracy and surface completeness, and between total distance traveled and execution time.

An Object SLAM Framework for Association, Mapping, and High-Level Tasks

Object SLAM is considered increasingly significant for robot high-level perception and decision-making. Existing studies fall short in terms of data association, object representation, and semantic mapping and frequently rely on additional assumptions, limiting their performance. In this paper, we present a comprehensive object SLAM framework that focuses on object-based perception and object-oriented robot tasks. First, we propose an ensemble data association approach for associating objects in complicated conditions by incorporating parametric and nonparametric statistic testing. In addition, we suggest an outlier-robust centroid and scale estimation algorithm for modeling objects based on the iForest and line alignment. Then a lightweight and object-oriented map is represented by estimated general object models. Taking into consideration the semantic invariance of objects, we convert the object map to a topological map to provide semantic descriptors to enable multi-map matching. Finally, we suggest an object-driven active exploration strategy to achieve autonomous mapping in the grasping scenario. A range of public datasets and real-world results in mapping, augmented reality, scene matching, relocalization, and robotic manipulation have been used to evaluate the proposed object SLAM framework for its efficient performance.

Functional and structural synaptic remodeling mechanisms underlying somatotopic organization and reorganization in the thalamus

The somatosensory system organizes the topographic representation of body maps, termed somatotopy, at all levels of an ascending hierarchy. Postnatal maturation of somatotopy establishes optimal somatosensation, whereas deafferentation in adults reorganizes somatotopy, which underlies pathological somatosensation, such as phantom pain and complex regional pain syndrome. Here, we focus on the mouse whisker somatosensory thalamus to study how sensory experience shapes the fine topography of afferent connectivity during the critical period and what mechanisms remodel it and drive a large-scale somatotopic reorganization after peripheral nerve injury. We will review our findings that, following peripheral nerve injury in adults, lemniscal afferent synapses onto thalamic neurons are remodeled back to immature configuration, as if the critical period reopens. The remodeling process is initiated with local activation of microglia in the brainstem somatosensory nucleus downstream to injured nerves and heterosynaptically controlled by input from GABAergic and cortical neurons to thalamic neurons. These fruits of thalamic studies complement well-studied cortical mechanisms of somatotopic organization and reorganization and unveil potential intervention points in treating pathological somatosensation.

Mapping representations in qualitative case studies: can we adapt Boisot’s I-Space model?

PurposeThis paper aims to consider ways to visually model data generated by qualitative case studies, pointing out a need for visualizations that depict both synchronic relations across representations and how those relations change diachronically. To develop an appropriate modeling approach, the paper critically examines Max Boisot’s I-Space model, a conceptual model for understanding the interplay among knowledge assets used by a population. I-Space maps information in three dimensions (abstraction, codification and diffusion). It is not directly adoptable for case study methodology due to three fundamental disjunctures: in theory, methodology and unit of analysis. However, it can be adapted for qualitative research by substituting analogues for abstraction, codification and diffusion.Design/methodology/approachUsing an example from early-stage technology entrepreneurship, this paper first reviews network, flow and matrix models used to systematically visualize case study data. It then presents Boisot’s I-Space model and critiques it from the perspective of qualitative workplace studies. Finally, it adapts the model using measures that have been used in qualitative case studies.FindingsThis paper notes three limitations of the I-Space model when applied to empirical cases of workplace learning. Its theory of information does not account well for how people use representations synchronically for learning. It is a conceptual framework, and the tentative attempts to use it for mapping representations have been used in workshops, not for systematically collected data. It does not adequately bound a case for analysis. Thus, it can be applied analogically but not directly for mapping representations in qualitative case studies.Practical implicationsThis paper identifies a possible way to develop I-Space for strategically mapping representations in qualitative case studies, using measures analogous to the I-Space axes to reflect observable behavior.Originality/valueIn providing a methodological critique for one model of knowledge management, this paper also develops criteria for appropriate modeling of meaningful artifacts in the context of qualitative studies of workplaces.

FEFR: Feature early fusion and reconstruction for online knowledge distillation

Online knowledge distillation breaks the pre-determined strong and weak teacher-student models, it provides a new way of thinking about knowledge distillation. However, the current online methods often use the Logits-based prediction distribution, and the features containing rich semantic information are rarely used. Even if the feature-based methods are used, they only operate on the last layer of the network, without further exploring the representation knowledge of the middle layer feature map. To address the above issues, we propose an innovative feature early fusion and reconstruction (FEFR) method for online knowledge distillation which entails four essential components: multi-scale feature extraction and intermediate layer feature early fusion, reconstruction of features, dual-attention and overall fusion module in this paper. We propose early fusion by “sum” operation for feature matrices between different layers and advance fusion to improve the feature map representation. In order to enhance the communication ability between groups to obtain features, the features were reconstructed. We create a dual-attention to enhance the critical channel and spatial regions adaptively in order to collect more accurate information. The previously processed feature maps are combined and fused using feature fusion, which also aids in student models training. A study of the network architectures of CIFAR-10, CIFAR-100, CINIC-10 and ImageNet 2012 shows that FEFR provides more useful characterization knowledge for refinement and improves accuracy by about 0.5% compared to other methods.

The New Zealand Community Fault Model – version 1.0: an improved geological foundation for seismic hazard modelling

ABSTRACT The New Zealand Community Fault Model (NZ CFM) is a publicly available representation of New Zealand fault zones that have the potential to produce damaging earthquakes. Compiled through collaborative engagement between New Zealand earthquake-science experts, this first edition (version 1.0) of the NZ CFM builds upon previous compilations of earthquake-source active fault models with the addition of new and modified information. Developed primarily to support an update of the New Zealand National Seismic Hazard Model, the NZ CFM comprises two principal components. The first dataset is a two-dimensional map representation of the surface traces of 880 generalised fault zones. Each fault zone is assigned specific geometric and kinematic attributes, including uncertainties, supplemented with a subjective quality ranking focused primarily on the confidence in assigned slip rates. The second component is a three-dimensional representation of the fault zones as triangulated mesh surfaces that are projected down-dip from the two-dimensional mapped traces to a geophysically-defined maximum fault rupture depth. This article summarises the compilation and parameterisation of the NZ CFM, along with background on its relation to predecessor datasets, and forward applications to probabilistic seismic hazard assessment and physics-based earthquake models currently being developed for Aotearoa New Zealand.

Feature evolvable learning with image streams

Feature Evolvable Stream Learning (FESL) has received extensive attentions during the past few years where old features could vanish and new features could appear when learning with streaming data. Existing FESL algorithms are mainly designed for simple datasets with low-dimension features, nevertheless they are ineffective to deal with complex streams such as image sequences. Such crux lies in two facts: (1) the shallow model, which is supported to be feasible for the low-dimension streams, fails to reveal the complex nonlinear patterns of images, and (2) the linear mapping used to recover the vanished features from the new ones is inadequate to reconstruct the old features of image streams. In response, this paper explores a new online learning paradigm: Feature Evolvable Learning with Image Streams (FELIS) which attempts to make the online learners less restrictive and more applicable. In particular, we present a novel ensemble residual network (ERN), in which the prediction is weighted combination of classifiers learnt by the feature representations from several residual blocks, such that the learning is able to start with a shallow network that enjoys fast convergence, and then gradually switch to a deeper model when more data has been received to learn more complex hypotheses. Moreover, we amend the first residual block of ERN as an autoencoder, and then proposed a latent representation mapping (LRM) approach to exploit the relationship between the previous and current feature space of the image streams via minimizing the discrepancy of the latent representations from the two different feature spaces. We carried out experiments on both virtual and real scenarios over large-scale images, and the experimental results demonstrate the effectiveness of the proposed method.

Emergent magnetic field and vector potential of the toroidal magnetic hopfions

Magnetic hopfions are localized magnetic solitons with non-zero 3D topological charge (Hopf index). Here I present an analytical calculation of the toroidal magnetic hopfion vector potential, emergent magnetic field, the Hopf index, and the magnetization configuration. The calculation method is based on the concept of the spinor representation of the Hopf mapping. The hopfions with arbitrary values of the azimuthal and poloidal vorticities are considered. The special role of the toroidal coordinates and their connection with the emergent vector potential gauge are demonstrated. The hopfion magnetization field is found explicitly for the arbitrary Hopf indices. It is shown that the Hopf charge density can be represented as a Jacobian of the transformation from the toroidal to the cylindrical coordinates.

Deep learning-based counter anti-forensic of GAN-based attack in HEVC compressed domain using coding pattern analysis

Double compression detection is one of the most popular techniques for the authentication of images and videos in the sphere of multimedia forensics. Several forensic methods have been suggested revealing video forgery, particularly high-efficiency video codec (HEVC) double compression detection. Even though forensic methods can effectively validate the integrity of videos, they become more intricate while practicing anti-forensic (AF) on manipulated videos. In this study, we present an innovative counter-anti-forensic (CAF) approach to detect anti-forensically misguided HEVC double compressed videos with the same coding parameters using coding pattern analysis. We mainly focus on employing generative adversarial networks (GANs) attacks to reconstruct artifacts that appear in compressed videos for deceiving forensic methods. In the proposed CAF method, we apply AF attacks twice in primary and secondary compressions and analyze the coding pattern changes to build a CAF method. We extract syntax information, and introduce new syntax map representations to improve distinguishability between single and double-compressed sequences. Additionally, we apply Gaussian and median filtering as AF attacks to show the robustness of the proposed method. The proposed deep learning-based CAF method shows better performance and can overwhelm state-of-the-art works.

Exploiting semantic information in a spiking neural SLAM system.

To navigate in new environments, an animal must be able to keep track of its position while simultaneously creating and updating an internal map of features in the environment, a problem formulated as simultaneous localization and mapping (SLAM) in the field of robotics. This requires integrating information from different domains, including self-motion cues, sensory, and semantic information. Several specialized neuron classes have been identified in the mammalian brain as being involved in solving SLAM. While biology has inspired a whole class of SLAM algorithms, the use of semantic information has not been explored in such work. We present a novel, biologically plausible SLAM model called SSP-SLAM-a spiking neural network designed using tools for large scale cognitive modeling. Our model uses a vector representation of continuous spatial maps, which can be encoded via spiking neural activity and bound with other features (continuous and discrete) to create compressed structures containing semantic information from multiple domains (e.g., spatial, temporal, visual, conceptual). We demonstrate that the dynamics of these representations can be implemented with a hybrid oscillatory-interference and continuous attractor network of head direction cells. The estimated self-position from this network is used to learn an associative memory between semantically encoded landmarks and their positions, i.e., an environment map, which is used for loop closure. Our experiments demonstrate that environment maps can be learned accurately and their use greatly improves self-position estimation. Furthermore, grid cells, place cells, and object vector cells are observed by this model. We also run our path integrator network on the NengoLoihi neuromorphic emulator to demonstrate feasibility for a full neuromorphic implementation for energy efficient SLAM.

Surrounding-aware representation prediction in Birds-Eye-View using transformers.

Birds-Eye-View (BEV) maps provide an accurate representation of sensory cues present in the surroundings, including dynamic and static elements. Generating a semantic representation of BEV maps can be a challenging task since it relies on object detection and image segmentation. Recent studies have developed Convolutional Neural networks (CNNs) to tackle the underlying challenge. However, current CNN-based models encounter a bottleneck in perceiving subtle nuances of information due to their limited capacity, which constrains the efficiency and accuracy of representation prediction, especially for multi-scale and multi-class elements. To address this issue, we propose novel neural networks for BEV semantic representation prediction that are built upon Transformers without convolution layers in a significantly different way from existing pure CNNs and hybrid architectures that merge CNNs and Transformers. Given a sequence of image frames as input, the proposed neural networks can directly output the BEV maps with per-class probabilities in end-to-end forecasting. The core innovations of the current study contain (1) a new pixel generation method powered by Transformers, (2) a novel algorithm for image-to-BEV transformation, and (3) a novel network for image feature extraction using attention mechanisms. We evaluate the proposed Models performance on two challenging benchmarks, the NuScenes dataset and the Argoverse 3D dataset, and compare it with state-of-the-art methods. Results show that the proposed model outperforms CNNs, achieving a relative improvement of 2.4 and 5.2% on the NuScenes and Argoverse 3D datasets, respectively.

3D-deep optical learning: a multimodal and multitask reconstruction framework for optical molecular tomography.

Optical molecular tomography (OMT) is an emerging imaging technique. To date, the poor universality of reconstruction algorithms based on deep learning for various imaged objects and optical probes limits the development and application of OMT. In this study, based on a new mapping representation, a multimodal and multitask reconstruction framework-3D deep optical learning (3DOL), was presented to overcome the limitations of OMT in universality by decomposing it into two tasks, optical field recovery and luminous source reconstruction. Specifically, slices of the original anatomy (provided by computed tomography) and boundary optical measurement of imaged objects serve as inputs of a recurrent convolutional neural network encoded parallel to extract multimodal features, and 2D information from a few axial planes within the samples is explicitly incorporated, which enables 3DOL to recognize different imaged objects. Subsequently, the optical field is recovered under the constraint of the object geometry, and then the luminous source is segmented by a learnable Laplace operator from the recovered optical field, which obtains stable and high-quality reconstruction results with extremely few parameters. This strategy enable 3DOL to better understand the relationship between the boundary optical measurement, optical field, and luminous source to improve 3DOL's ability to work in a wide range of spectra. The results of numerical simulations, physical phantoms, and in vivo experiments demonstrate that 3DOL is a compatible deep-learning approach to tomographic imaging diverse objects. Moreover, the fully trained 3DOL under specific wavelengths can be generalized to other spectra in the 620-900 nm NIR-I window.

It is time to revisit and re-evaluate Africa’s sub-par emergency surgical delivery

It is time to revisit and re-evaluate Africa’s sub-par emergency surgical delivery